JP7228780B2 - Equipment diagnosis system and equipment diagnosis method - Google Patents

Description

The present invention relates to an equipment diagnosis system and an equipment diagnosis method.

In Patent Document 1, feeders of mounting machines in multiple factories are monitored, and if specific parameters such as the number of times of supply or the number of times of supply errors exceed a threshold, use is prohibited, and the prohibited feeders are repaired or inspected at a maintenance center. shown to do.

Japanese Patent Application Laid-Open No. 2004-140162

However, even if a specific parameter such as the number of feeds or the number of feed failures exceeds the threshold, the mounter is not malfunctioning, and repair or inspection (hereinafter referred to as maintenance) of the mounter may not necessarily be necessary. . And maintenance of manufacturing equipment such as mounting machines that are not malfunctioning and do not require maintenance not only results in useless work, but also wastes parts, resulting in wasted costs (loss costs). turn into.

SUMMARY OF THE INVENTION Accordingly, it is an object of the present invention to provide an equipment diagnosis system and an equipment diagnosis method capable of more appropriately diagnosing malfunctions of manufacturing equipment.

In order to achieve the above object, an equipment diagnosis system according to one aspect of the present invention includes an operation information acquisition unit that acquires operation information of each of a plurality of manufacturing equipment; an output unit for diagnosing a malfunction of at least one manufacturing facility; an output unit for outputting a diagnosis result of the malfunction diagnosis unit; and a feedback information acquisition unit for acquiring feedback information on the diagnosis result.

Each of these specific aspects may be realized by an apparatus, method, integrated circuit, computer program, or recording medium, and may be realized by any combination of the apparatus, method, integrated circuit, computer program, and recording medium. may

According to the equipment diagnosis system and the like of the present invention, it is possible to more appropriately diagnose malfunctions of manufacturing equipment.

1 is a diagram showing an example of a configuration of an equipment diagnosis system according to Embodiment 1; FIG. 2 is a diagram showing an example of a detailed configuration of a malfunction diagnosis section shown in FIG. 1; FIG. It is a figure which shows the structure of the diagnostic model shown in FIG. 2, and a learning method. 4 is a flow chart showing the flow of operation of the equipment diagnostic system according to Embodiment 1. FIG. 4 is a flow chart showing the operation flow of the equipment diagnosis system including processing for updating a diagnosis model according to Embodiment 1. FIG. FIG. 10 is a diagram showing an example of the configuration of an equipment diagnosis system according to Embodiment 2; FIG. FIG. 10 is a diagram showing an equipment diagnosis system in an example of the second embodiment; FIG. FIG. 8 is a plan view of the mounting machine shown in FIG. 7; FIG. 9 is a perspective view of the tape feeder shown in FIG. 8; FIG. 9 is a side view showing the configuration of the tape feeder shown in FIG. 8; 8 is a diagram showing an example of subjective data shown in FIG. 7; FIG. FIG. 8 is a diagram showing an example of maintenance performance data shown in FIG. 7; FIG.

In order to achieve the above object, an equipment diagnosis system according to an aspect of the present invention includes an operation information acquisition unit that acquires operation information of each of a plurality of manufacturing equipment; an output unit for diagnosing a malfunction of at least one manufacturing facility; an output unit for outputting a diagnosis result of the malfunction diagnosis unit; and a feedback information acquisition unit for acquiring feedback information on the diagnosis result.

According to this, malfunction of the manufacturing equipment can be diagnosed more appropriately.

Further, a maintenance unit may be provided that performs maintenance of the at least one manufacturing facility diagnosed as malfunctioning by the malfunction diagnosis unit.

Further, the apparatus may further include a maintenance work instructing section that instructs the maintenance section to perform maintenance of the at least one manufacturing facility diagnosed as malfunctioning by the malfunction diagnosis section.

Further, the apparatus may further include a maintenance work instructing section that instructs the user to perform maintenance on the at least one manufacturing facility diagnosed as malfunctioning by the malfunction diagnosis section.

Further, the feedback information may include at least one of information on the maintenance result of the at least one manufacturing facility by the maintenance department and information on the maintenance result by the confirmer.

Further, the feedback information may include information regarding whether or not the malfunction diagnosis by the malfunction diagnosis unit is correct.

Further, the malfunction diagnosis unit may have a diagnosis model, and the diagnosis model may be updated based on the feedback information.

Further, the diagnostic model may be created by learning using operation record data including operation information of each of the plurality of manufacturing equipment.

The apparatus may further include an equipment recovery instruction section that instructs a recovery person to recover the at least one manufacturing facility diagnosed by the malfunction diagnosis section as malfunctioning.

Further, the manufacturing equipment may include a surface mounting device or at least one element among a plurality of elements constituting the surface mounting device.

Also, the diagnostic model may be one of a plurality of diagnostic models, and the plurality of diagnostic models may be provided corresponding to a plurality of customers.

It should be noted that these general or specific aspects may be realized by an apparatus, method, integrated circuit, computer program, or recording medium such as a computer-readable CD-ROM. , may be implemented in any combination of a computer program and a recording medium.

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. It should be noted that each of the embodiments described below is a specific example of the present invention. Numerical values, shapes, materials, components, arrangement positions and connection forms of components, usage procedures, communication procedures, and the like shown in the following embodiments are examples, and are not intended to limit the present invention. In addition, among the constituent elements in the following embodiments, constituent elements that are not described in independent claims representing the highest concept of the present invention will be described as optional constituent elements. Also, each figure is not necessarily strictly illustrated. In each figure, substantially the same configurations are denoted by the same reference numerals, and overlapping descriptions are omitted or simplified.

(Embodiment 1)
The equipment diagnosis system according to the first embodiment will be described below with reference to the drawings.

[Configuration of Equipment Diagnosis System 10]
FIG. 1 is a diagram showing an example of the configuration of an equipment diagnosis system 10 according to Embodiment 1. As shown in FIG. The equipment diagnosis system 10 in this embodiment can diagnose malfunctions of manufacturing equipment. The facility diagnosis system 10 includes an operation information acquisition unit 11, a malfunction diagnosis unit 12, an output unit 13, and a feedback information acquisition unit 14, as shown in FIG.

[Operation information acquisition unit 11]
The operation information acquisition unit 11 acquires operation information of each of a plurality of manufacturing facilities. The operation information acquisition unit 11 is implemented by a computer including, for example, a processor (microprocessor), memory, communication interface, and the like. In the present embodiment, the operation information acquisition unit 11 periodically collects or acquires operation information (operation data) of manufacturing equipment.

Here, the manufacturing equipment is mounting equipment, and includes at least one element of a surface mounting device or a plurality of elements that constitute the surface mounting device. The manufacturing equipment is not limited to these cases, and may be equipment, devices, etc. for manufacturing mounted boards, parts, etc. in a factory, as long as it can acquire operation data indicating one or more parameters during operation. .

The operation information is production performance system feature data in one mounting facility, and is log data such as correction amount, pickup learning value, component pickup event, error occurrence event, and the like. When acquiring the operation information, the operation information acquiring unit 11 also acquires or stores the date and time (sampling date and time) when each piece of data of the production performance system feature data is acquired. The correction amount is the correction amount of the pickup position when the mounting equipment picks up the electronic component. This correction amount, that is, the suction correction amount is calculated for each component recognition result. Also, the suction learning value is an offset amount when the suction nozzle picks up the component from the feeder. This adsorption learning value is changed by learning a prescribed offset value from the recognition results of a plurality of times so far. The component pick-up count is the number of pick-ups of components from each feeder. The component pick-up count is obtained from the log data of the component pick-up event. The number of component pick-up times is the number of times components are picked up from each component supply device (feeder). It may be the number of times the component is picked up in an arbitrary period. The date from which the component pick-up frequency is calculated is not limited to these examples, and may be determined according to the application. The number of error occurrences is the number of times the pickup nozzle in the mounting equipment failed to pick up the component. The number of error occurrences is obtained from the log data of the error occurrence event. The date from which the number of error occurrences is calculated may also be determined according to the application, similar to the date from which the number of component pickups is calculated. The production performance system feature data is hereinafter also referred to as operation data.

It should be noted that the operation information is not limited to the production record system feature data for one mounting facility, and may further include profile system feature data for one mounting facility. The profile system feature data includes the date and time when the component (reel) is attached to the feeder (time stamp), the component dimensions, the component vendor of the component supplied from the feeder, the maintenance date and time of the mounting equipment, and the mounting equipment This includes past failure locations, sprocket offsets in mounting equipment, and feeder cover heights in mounting equipment. The production performance feature data, excluding subjective data and parts vendors, is hereinafter also referred to as maintenance performance data.

[Malfunction diagnosis unit 12]
The malfunction diagnosis unit 12 diagnoses malfunction of at least one of the plurality of manufacturing equipment based on the operation information acquired by the operation information acquisition unit 11 . The malfunction diagnosis unit 12 is implemented by a computer including, for example, a processor (microprocessor), memory, communication interface, and the like. In the present embodiment, the malfunction diagnosis unit 12 uses the diagnostic model 121 to estimate malfunction levels of units (elements) constituting each of a plurality of mounting equipment from the operation information acquired by the operation information acquisition unit 11. and diagnose malfunctions in at least one manufacturing facility. Here, the unit may be, for example, a tape feeder that constitutes the mounting equipment, or may be a recognition camera, a mounting head, or the like. Also, it may be a component such as a gear or a motor that constitutes the tape feeder.

FIG. 2 is a diagram showing an example of a detailed configuration of the malfunction diagnosis section 12 shown in FIG. FIG. 3 is a diagram showing the configuration and learning method of the diagnostic model 121 shown in FIG. The malfunction diagnosis unit 12 includes a diagnosis model 121, a pattern comparison unit 123, and a level determination unit 124, as shown in FIG.

<Diagnostic model 121>
The diagnostic model 121 is created by learning operation performance data indicating operation information of each of a plurality of manufacturing facilities. Diagnostic model 121 is updated based on the feedback information.

In this embodiment, the diagnostic model 121 determines whether the behavior of the units constituting each of the plurality of mounting equipments indicated by the feature data included in the operation information of each of the plurality of manufacturing equipments to be monitored is in a normal state. It is a model that can show The diagnostic model 121 is composed of a normal behavior pattern 1211, a normal correlation pattern 1212, and a preprocessing section 1213, as shown in FIG. 3, for example.

The preprocessing unit 1213 performs a process of accumulating production performance system feature data such as operation performance data acquired by the operation information acquiring unit 11 . From this, the normal behavior pattern 1211 can be learned or additionally learned (updated) using a certain amount accumulated for at least one day or one week. For example, the preprocessing unit 1213 obtains the number of component pickup times from the log data of the component pickup event acquired by the operation information acquisition unit 11, and accumulates it as production performance system feature data. Similarly, the preprocessing unit 1213, for example, obtains the number of error occurrences from the error occurrence event log data acquired by the operation information acquisition unit 11, and accumulates it as production performance system feature data.

The normal behavior pattern 1211 is a statistical pattern showing how the production performance feature data fluctuates if the unit behaves normally, using the production performance feature data for at least one day or one week. It is created by subjective machine learning. For example, normal behavior patterns 1211 may be probabilistic patterns obtained, for example, from logistic regression analysis. In addition, the normal behavior pattern 1211 is additionally learned, ie, updated, based on subjective data, which is feedback information for diagnosis results, and which has accumulated a certain amount.

The subjective data is data subjectively determined by a user or the like as to whether or not the malfunction diagnosis by the malfunction diagnosis unit 12 is correct. It should be noted that, based on the subjective data, the threshold described below may be changed without changing the normal behavior pattern 1211 . This makes it possible to reflect unit-to-unit variation based on the subjective data of a person involved in the maintenance of the unit, so that it is possible to more accurately estimate the unit that requires maintenance. Note that variations between units are differences depending on how the unit is used, and mean that the need for maintenance differs even if the same correction amount, number of error occurrences, or number of component pickups are indicated. The subjective data may also include visual inspection data indicating the result of visual inspection of the maintenance target.

The normal correlation pattern 1212 shows what kind of variation there is if the behavior of the unit is normal when it is divided into parts, offsets, etc. by using the production performance system feature data and the profile system feature data. It's a pattern. Normal correlation patterns 1212 are also probabilistic patterns created by statistical machine learning, for example, obtained from logistic regression analysis. In addition, the normal correlation pattern 1212 is additionally learned or updated based on subjective data and maintenance performance data, which are feedback information for diagnostic results, and which have accumulated a certain amount.

The maintenance performance data includes at least one of information regarding the result of maintenance of the unit diagnosed as malfunctioning by the malfunction diagnosis unit 12 and information regarding the result of maintenance performed by the confirmer. The maintenance result information includes, for example, the date and time when maintenance was performed, past failure points, maintenance items, etc. The maintenance items include, for example, sprocket offset and feeder cover height. In addition, as information on maintenance results, include the serial number of the unit, the date and time when the unit was received, the person in charge of maintenance and the date and time of implementation, the state before maintenance, the items performed during maintenance, the results of adjustments and measurements, and the date and time when the unit was returned. may This makes it possible to reflect unit-to-unit variations based on the maintenance record data, so it is possible to more accurately estimate malfunctioning units that require maintenance. The confirmer is not limited to the user who manages one manufacturing facility, and may be a service person such as a service person who performs maintenance.

<Pattern comparison unit 123>
The pattern comparison unit 123 compares the diagnostic model 121, which is the created learning model, with the feature data included in the operation information of the manufacturing equipment to be compared. More specifically, the pattern comparison unit 123 compares whether the feature data included in the operation information of the unit to be compared is included in the normal ranges indicated by the normal behavior pattern 1211 and the normal correlation pattern 1212 . As a result of the comparison, if the feature data is not included in the normal range, the pattern comparison section 123 outputs the difference to the level determination section 124 as an index.

<Level determination unit 124>
If the feature data is not included in the normal range as a result of the comparison by the pattern comparison unit 123, the level determination unit 124 estimates the disorder level depending on whether the difference exceeds a predetermined threshold. The level determination unit 124 determines that the disorder level is the abnormal level or the normal level depending on whether or not the threshold value is exceeded. Note that even if the threshold (first threshold) is not exceeded, the level determination unit 124 determines whether the level at which an abnormality may occur (that is, the abnormal grace level) or the normal level, depending on whether the second threshold is exceeded. It may be determined that there is

[Output unit 13]
The output unit 13 outputs the diagnosis result of the malfunction diagnosis unit 12 . The output unit 13 is implemented by, for example, a computer including a processor (microprocessor), memory, communication interface, and the like.

More specifically, the output unit 13 may be transmission means for transmitting the diagnosis result of at least one manufacturing facility diagnosed as malfunctioning by the malfunction diagnosis unit 12 to the user who manages the one manufacturing facility. In this case, the output unit 13 may be a server that transmits the diagnosis result by e-mail or the like. Further, the output unit 13 may be display means such as a display that displays the diagnosis result of at least one manufacturing facility diagnosed as malfunctioning by the malfunction diagnosis unit 12 .

[Feedback information acquisition unit 14]
The feedback information acquisition unit 14 acquires feedback information on the diagnosis result. The feedback information acquisition unit 14 is realized by, for example, a computer including a processor (microprocessor), memory, communication interface, and the like.

Here, the feedback information may include, for example, at least one of information regarding the maintenance result of the unit diagnosed as malfunctioning by the malfunction diagnosis section 12 and information regarding the user's maintenance result. In addition, the feedback information may further include subjectively determined validity such as information regarding whether or not the malfunction diagnosis by the malfunction diagnosis unit 12 is correct.

The maintenance result of the unit diagnosed as malfunctioning by the malfunction diagnosis unit 12 is, for example, maintenance performance data. As described above, the maintenance performance data includes, for example, the serial number of the unit, the date and time when the unit was received, the person in charge of maintenance and the date and time when the maintenance was performed, the state before maintenance, the items to be performed during maintenance, the results of adjustment and measurement, and the return of the unit. May include date and time. In this way, the maintenance record data includes information indicating the results of maintenance performed on each maintenance target unit.

[Operation of Equipment Diagnosis System 10]
The operation of the facility diagnosis system 10 configured as above will be described below.

FIG. 4 is a flow chart showing the flow of operation of the equipment diagnosis system 10 according to the first embodiment.

First, the facility diagnosis system 10 acquires operation information of each of the plurality of manufacturing facilities (S11).

Next, the facility diagnostic system 10 diagnoses malfunction of at least one of the plurality of manufacturing facilities based on the operation information acquired in step S11 (S12). In the present embodiment, the equipment diagnosis system 10 estimates the malfunction level of each unit (element) constituting each of the plurality of mounting equipment using the diagnosis model 121, for example, based on the operation information acquired in step S11.

Next, the equipment diagnosis system 10 outputs the diagnosis result diagnosed in step S11 (S13). For example, the equipment diagnosis system 10 transmits the diagnosis result of at least one manufacturing equipment diagnosed as malfunctioning by the malfunction diagnosis unit 12 to the user who manages the one manufacturing equipment.

Next, the equipment diagnosis system 10 acquires feedback information on the diagnosis result (S14). In this embodiment, when a certain amount of feedback information such as at least one day's worth or one week's worth of feedback information is accumulated, the equipment diagnosis system 10 updates the diagnosis model 121 using the accumulated feedback information. The operation flow of the equipment diagnosis system 10 including the process of updating the diagnosis model 121 will be described below.

FIG. 5 is a flow chart showing the operation flow of the equipment diagnosis system 10 including the process of updating the diagnosis model 121 according to the first embodiment. Elements similar to those in FIG. 4 are denoted by the same reference numerals, and detailed description thereof will be omitted.

That is, in step S15, the facility diagnosis system 10 determines whether or not the acquired feedback information exceeds a predetermined amount (S15). In the present embodiment, the equipment diagnostic system 10 determines whether or not a certain amount of feedback information, such as at least one day's worth or one week's worth, has been accumulated.

Next, the facility diagnostic system 10 updates the diagnostic model 121 based on the acquired feedback information (S16). In this embodiment, the diagnostic model 121 is updated using feedback information accumulated in a certain amount. As a result, by using the feedback information, it is possible to reflect the unit-to-unit variation due to the difference in the method of use, etc. in the diagnostic model 121, so that it is possible to more accurately estimate the units that require maintenance.

[Effects, etc.]
As described above, the facility diagnostic system 10 of the present embodiment not only diagnoses malfunction of at least one of the plurality of manufacturing facilities based on the operation information of each of the plurality of manufacturing facilities, but also provides feedback information. to get By using the acquired feedback information, it is possible to take into account variations among multiple manufacturing facilities due to differences in usage. This makes it possible to more accurately diagnose malfunction of at least one manufacturing facility that truly requires maintenance, and to more appropriately diagnose malfunction of the manufacturing facility.

Here, the equipment diagnosis system 10 of the present embodiment uses the diagnosis model 121 created in advance to perform malfunction diagnosis of the manufacturing equipment from the operation information of each of the plurality of manufacturing equipment to be monitored. The diagnostic model 121 created in advance is created by performing statistical machine learning in advance using operation data of a plurality of manufacturing facilities. The diagnosis model 121 created in advance corresponds to a learning model common to a plurality of manufacturing equipments, such as being learned by treating different elements for each manufacturing equipment as constants.

Furthermore, the diagnostic model 121 is updated (additionally learned) using a certain amount of accumulated feedback information. As a result, the feedback information can be used to reflect in the diagnostic model 121 variations among the plurality of manufacturing facilities due to differences in usage methods and the like. As a result, malfunctions of manufacturing equipment that truly require maintenance can be diagnosed more accurately. The updated diagnostic model 121 is created by statistical machine learning using feedback information such as subjective data and maintenance performance data. The updated diagnostic model 121 uses data or values indicating different elements for each manufacturing facility, and additionally learns by substituting the points handled as constants in the diagnostic model 121 created in advance. It corresponds to a learning model optimized for each facility.

(Embodiment 2)
Although Embodiment 1 has been described based on the minimum configuration of the equipment diagnosis system, the present invention is not limited to this. It may further include a configuration for performing maintenance on the manufacturing facility diagnosed as malfunctioning. This case will be described as a second embodiment. In addition, below, it demonstrates centering on a different point from Embodiment 1. FIG.

[Configuration of Equipment Diagnosis System 20]
FIG. 6 is a diagram showing an example of the configuration of the equipment diagnosis system 20 according to the second embodiment. Elements similar to those in FIG. 1 are denoted by the same reference numerals, and detailed description thereof will be omitted. As shown in FIG. 6, the equipment diagnosis system 20 includes an operation information acquisition unit 11, a malfunction diagnosis unit 12, an output unit 13, a feedback information acquisition unit 14, an equipment recovery instruction unit 25, and a maintenance work instruction unit 26. and a maintenance unit 27 . Compared to the equipment diagnosis system 10 in Embodiment 1, the equipment diagnosis system 20 has an additional configuration of an equipment collection instruction unit 25, a maintenance work instruction unit 26, and a maintenance unit 27. The configuration of the part 22 is different.

[Malfunction diagnosis unit 22]
The malfunction diagnosis unit 22 diagnoses malfunction of at least one of the plurality of manufacturing equipment based on the operation information acquired by the operation information acquisition unit 11 . More specifically, the malfunction diagnosis unit 22 uses the diagnostic model 121 to estimate malfunction levels of units (elements) that constitute each of the plurality of mounting equipment from the operation information acquired by the operation information acquisition unit 11. , to diagnose malfunctions in at least one manufacturing facility.

Here, the malfunction diagnosing section 22 according to the second embodiment differs from the malfunction diagnosing section 12 according to the first embodiment in the diagnostic model 121 to be used. That is, the diagnostic model 121 is one of a plurality of diagnostic models provided corresponding to a plurality of customers. More specifically, the diagnosis model 121 is first learned using operation record data indicating operation information of each of a plurality of manufacturing facilities so as to commonly correspond to a plurality of customers. is a model. Then, the diagnostic model 121 is an individual learning updated by learning using operation record data indicating operation information of each of a plurality of manufacturing facilities belonging to the six customers so as to correspond to one customer. is a model. Note that this diagnostic model 121 is then further updated based on feedback information on the diagnostic results of a plurality of manufacturing facilities belonging to the one customer, and becomes an optimized learning model.

Since the rest of the configuration is the same as that of the malfunction diagnosis section 12 in the first embodiment, description thereof will be omitted.

[Equipment Recovery Instruction Unit 25]
The equipment collection instruction unit 25 instructs a collection person to collect at least one piece of manufacturing equipment that has been diagnosed as malfunctioning by the malfunction diagnosis unit 22 .

In the present embodiment, the facility recovery instruction unit 25 instructs a recovery person to recover the at least one unit based on the diagnosis result of each unit that constitutes each of the at least one manufacturing facility that has been diagnosed as malfunctioning by the malfunction diagnosis unit 12. instruct. Here, the collector is a person in charge of the sales company that sells the at least one unit or a person who is requested by the sales company to collect the unit. The sales company may be a manufacturer of the at least one manufacturing equipment or unit, or a company that sells the at least one manufacturing equipment or unit.

[Maintenance work instruction unit 26]
The maintenance work instruction unit 26 instructs the maintenance unit 27 to perform maintenance on at least one piece of manufacturing equipment that has been diagnosed as malfunctioning by the malfunction diagnosis unit 22 . Further, the maintenance work instruction unit 26 may instruct the user to perform maintenance on at least one piece of manufacturing equipment that has been diagnosed as malfunctioning by the malfunction diagnosis unit 22 .

In the present embodiment, the maintenance work instructing section 26 instructs maintenance of at least one unit, based on the diagnosis result of each unit constituting at least one manufacturing facility diagnosed as malfunctioning by the malfunction diagnosis section 12 . When the maintenance section 27 performs maintenance on the at least one unit, the maintenance work instruction section 26 may instruct the maintenance section 27 to perform maintenance on the at least one unit. When maintenance on the at least one unit is performed by a user or the like, the maintenance work instruction unit 26 may instruct the user to perform maintenance on the at least one unit.

[Maintenance Department 27]
The maintenance unit 27 performs maintenance on at least one piece of manufacturing equipment that has been diagnosed as malfunctioning by the malfunction diagnosis unit 22 .

More specifically, the maintenance section 27 controls the automatic maintenance device, and causes the automatic maintenance device to perform maintenance when the at least one unit can be maintained. Here, the automatic maintenance device is a device that performs inspection by measuring various states of a unit that constitutes the at least one manufacturing facility, and that repairs or replaces the unit. Note that the automatic maintenance device may perform only inspection among inspection, repair, and replacement, and repair and replacement may be performed by a user such as a person in charge of maintenance.

[Effects, etc.]
As described above, the equipment diagnosis system 20 of the present embodiment is based on the operation information of each of the plurality of manufacturing equipment belonging to the one customer, at least one of the plurality of manufacturing equipment belonging to the one customer. Get feedback information in addition to diagnosing equipment malfunctions. Further, by using the feedback information for the diagnostic results of the plurality of manufacturing facilities belonging to the one customer, it is possible to consider the variations in each of the plurality of manufacturing facilities belonging to the one customer. As a result, out of the plurality of manufacturing facilities belonging to the one customer, it becomes possible to more accurately diagnose malfunctions of at least one manufacturing facility that truly requires maintenance, and to more appropriately diagnose malfunctions of manufacturing facilities. Diagnose.

In addition, the equipment diagnosis system 20 of the present embodiment uses the diagnosis model 121 created in advance to perform malfunction diagnosis of the manufacturing equipment from the operation information of each of the plurality of manufacturing equipment to be monitored belonging to the one customer. The diagnostic model 121 created in advance is created by performing statistical machine learning in advance using operation data of a plurality of manufacturing facilities belonging to the one customer. The diagnostic model 121 created in advance corresponds to a learning model common to a plurality of manufacturing facilities belonging to the one customer.

Furthermore, the diagnosis model 121 is updated (additionally learned) using feedback information on diagnosis results of a plurality of manufacturing facilities belonging to the customer, which is accumulated in a certain amount. As a result, it is possible to reflect in the diagnosis model 121 variations in each of the plurality of manufacturing facilities due to differences in usage, etc., using the feedback information on the diagnostic results of the plurality of manufacturing facilities belonging to the one customer. As a result, out of the plurality of manufacturing facilities belonging to the one customer, it is possible to more accurately diagnose the malfunction of the manufacturing facility that truly requires maintenance. Note that the updated diagnostic model 121 is created by performing statistical machine learning using feedback information on diagnostic results of a plurality of manufacturing facilities belonging to the one customer, such as subjective data and maintenance performance data. The updated diagnostic model 121 corresponds to a learning model optimized for each manufacturing facility belonging to the one customer.

(Example)
As a specific example of the equipment diagnosis system 20 according to Embodiment 2, an equipment diagnosis system 20A in the surface mounting industry will be described with reference to FIG.

FIG. 7 is a diagram showing an equipment diagnosis system 20A in an example of the second embodiment.

The equipment diagnostic system 20A in the surface mounting industry is configured to diagnose malfunctions of a plurality of units that constitute a surface mounter as manufacturing equipment. The facility diagnostic system 20A shown in FIG. 7 includes a facility diagnostic device 10A, a server 13A, and a mail server 13B. Furthermore, as shown in FIG. 7, the facility diagnosis system 20A includes a mail server 25A, an automatic maintenance device 27A, a mounter 202, an LNB 203, a server 204, a database 205, an input device 206, and a data collection device. 207 and an input device 208 . These devices are physically located in one of a plurality of areas such as the area belonging to the diagnostic provider 201A, the cloud 201B, the area belonging to the sales company 201C, and the area belonging to the factory 201D.

<Diagnosis provider 201A>
The diagnostic provider 201A is, for example, a company that provides the results of diagnosing malfunctions of a plurality of units that constitute a surface mounter. A facility diagnostic device 10A, a server 13A, and a mail server 13B are physically arranged in the area to which the diagnostic provider 201A belongs.

10 A of equipment diagnostic apparatuses contain all the components which comprise the equipment diagnostic system 10, for example. In this embodiment, the facility diagnosis apparatus 10A uses the diagnosis model 121 to obtain the result of diagnosing malfunctions of each of the plurality of units constituting the mounter 202 based on the operation data of the mounter 202 collected at the factory 201D. output to 13A.

The diagnostic model 121 in this embodiment learns operation record data indicating operation information of the mounter 202, which is a manufacturing apparatus belonging to the factory 201D, so as to correspond to one customer. That is, the diagnostic model 121 is a model that can indicate whether or not the plurality of units that constitute the mounting machine 202 are in a normal state. The diagnostic model 121 is created by learning using operation record data as a standard recommended by the manufacturer of the mounter 202 . Therefore, the equipment diagnostic device 10A compares the diagnostic model 121 with the characteristic data obtained from the performance data of the mounting machine 202, which is the manufacturing equipment to be monitored, and indicates whether the difference exceeds a predetermined threshold. A diagnosis result is output to the server 13A.

After that, this diagnostic model 121 is updated based on the feedback information on the diagnostic result of the mounting machine 202 belonging to the factory 201D. In this embodiment, for example, if the feedback information for the diagnosis result of one unit is subjective data and indicates that the diagnosis result of the one unit is NG, the predetermined threshold is changed. On the other hand, for example, when the feedback information for the diagnosis result of one unit is subjective data and indicates that the diagnosis result of the one unit is OK, the actual malfunctioning part of the one unit and its behavior data is used as teacher data, the diagnostic model 121 is additionally learned (updated). As a result, the accuracy of malfunction diagnosis can be improved.

In addition, for example, when the feedback information for the diagnosis result of one unit is maintenance performance data, the diagnostic model 121 is additionally learned using the maintenance performance data including the actual malfunction location and behavior data of the one unit as teacher data. (Update. As a result, the accuracy of malfunction diagnosis can be improved. Further, for example, the feedback information for the diagnosis result of one unit may be subjective data and maintenance performance data.

The server 13A creates a report, such as a malfunction candidate unit list, from the diagnostic results output from the equipment diagnostic device 10A. The server 13A creates a report such as a malfunction candidate unit list by collecting and listing units determined to be malfunctioning in the diagnostic results.

The mail server 13B transmits a report such as the malfunction candidate unit list created by the server 13A to the mail server 25A. This makes it possible to notify the user who manages or maintains the mounter 202 in the factory 201D of the malfunction candidate unit.

<Cloud 201B>
The cloud 201B is a virtualization server that cooperates with various devices via the Internet. Cloud 201B provides on-demand use of IT resources such as databases via the Internet.

Database 205 is managed by cloud 201B. The database 205 accumulates operation data about the mounting machine 202 collected at the factory 201D. The database 205 also accumulates maintenance record data for the mounter 202 . Note that the database 205 may accumulate feedback information such as evaluations of users who manage or maintain the mounters 202 in the factory 201D with respect to the malfunction candidate unit list created by the server 13A.

<Factory 201D>
The factory 201D is, for example, a company that manufactures electronic components using manufacturing equipment. A mail server 25A, a mounter 202, an LNB 203, a server 204, and an input device 206 are physically arranged in the area to which the factory 201D belongs.

The mounter 202 is an example of manufacturing equipment and is a surface mounter. The mounter 202 is composed of a plurality of units, which are a plurality of elements.

An example of the configuration of the mounter 202 will now be described with reference to FIGS. 8 to 10. FIG. FIG. 8 is a plan view of the mounter 202 shown in FIG.

In the mounter 202 , a substrate fixing portion (conveyor rail) for fixing a circuit board 314 on which electronic components (not shown) are mounted is provided in the central portion of a base 313 . A pair of component mounting stages 315 are provided on the base 313 so as to be bilaterally symmetrical with the board fixing portion interposed therebetween.

A component mounting stage 315 includes a plurality of rows of tape feeders 310 that continuously supply electronic components, and enables a wide variety of electronic components to be picked up at the component supply position. The component mounting stage 315 has a suction head 316 that holds an electronic component at a component supply position and mounts the electronic component on the circuit board 314 .

The suction head 316 is supported by the XY robot 317 . The XY robot 317 can move in each of the XY directions in FIG. The XY robot 317 has an X-axis beam 318, and a suction head 316 is supported on this X-axis beam 318 so as to be movable in the X direction. X-axis beam 318 is movable in the Y direction along Y-axis beam 319 .

A nozzle change section 320 is provided between the component mounting stage 315 and the substrate fixing section. The nozzle change section 320 is provided with a nozzle holder 221 , a component recognition section 322 and a waste tray 323 .

The nozzle holder 221 stores component suction nozzles 324 for various electronic components to be mounted on the suction head 316 . The suction head 316 can replace the component suction nozzle 324 at the nozzle change section 320 . The component recognition unit 322 includes an optical sensor such as a line sensor, and recognizes the posture (component position, rotation angle, etc.) of the electronic component that is being sucked by the component suction nozzle 324 of the suction head 316 . If the electronic component picked up by the component pickup nozzle 324 of the pickup head 316 has an error in type or has a problem, the electronic component is discarded on the discard tray 323 .

Next, the configuration of the tape feeder 310 will be described.

FIG. 9 is a perspective view of tape feeder 310 shown in FIG. FIG. 10 is a side view showing the configuration of tape feeder 310 shown in FIG.

The tape feeder 310 is held by a cart (not shown), and is detachable from the mounter 202 by an operator operating the cart. A tape reel 325 shown in FIG. 9 is attached to the tape feeder 310, and a tape 326 shown in FIG. 10 containing electronic components at an equal pitch is wound. The tape feeder 310 has a function of feeding the tape 326 inside the outer frame 32 shown in FIG.

A tape feeding mechanism 329 is arranged at the leading end of the outer frame 327 . The tape feed mechanism 329 includes a sprocket 332 having pins formed on its outer circumference that engage feed holes formed at equal pitches in the feed direction of the tape 326 . The tape feed mechanism 329 includes a drive motor 333 that drives the sprocket 332, a transmission mechanism that transmits the rotational drive of the drive motor 333 to the sprocket 332, and a controller that controls the rotational drive of the drive motor 333. A feeder control unit 335 is provided.

When the drive motor 333 is controlled to rotate intermittently corresponding to the pitch of the electronic components, the sprocket 332 performs index rotation, and the tape 326 wound around the tape reel 325 is moved from the rear end to the outer frame 327. It is drawn in and pitch-fed to the tip. As a result, the electronic components stored on the tape 326 are sequentially supplied to the supply port 328, which is the pickup position.

The supply port 328 is formed by opening a portion of a tape guide 337 that is attached to the upper portion of the outer frame 327 and guides the feeding of the tape 326 . A part of the tape guide 337 is a folded portion of the cover tape 338 peeled from the surface of the tape 326 , and the cover tape 338 is peeled off from the surface of the tape 326 by the cover tape peeling mechanism 39 . As a result, the electronic component is supplied to the supply port 328 in an exposed state and picked up by the component suction nozzle 324 positioned above the supply port 328 .

The gear unit 340 is composed of a gear train of a drive gear 343 , a first transmission gear 345 , a second transmission gear 346 , a third transmission gear 347 and a final gear 342 . The drive gear 343 is attached to the output shaft of the drive motor 333 . A first transmission gear 345 meshes with the drive gear 343 . A second transmission gear 346 is coaxially fixed to the first transmission gear 345 and meshes with a third transmission gear 347 . The third transmission gear 347 meshing with the second transmission gear 346 also meshes with the final gear 342 fixed to the rotating shaft of the sprocket 332 .

In the tape feeder 310, when the electronic components contained in the tape 326 at equal pitches are sequentially supplied to the supply port 328, the stop position of the tape 326 or the sprocket 332 is set for each pitch feed so that the feed position does not vary. must be precisely adjusted.

Therefore, in the mounter 202 configured as described above, the causes of malfunctions are as follows, for example. Contamination of the supply port 328, wear and backlash of the tip of the sprocket 332, fatigue and feed position deviation of the drive motor 333, backlash and feed position deviation of the tape feeder 310, deterioration of motor torque, and the like.

Hereinafter, returning to FIG. 7, description will be continued.

The LNB 203 is also called a Line Network BOX, and is a device that collects data obtained when the mounter 202 is operating. The LNB 203 is implemented by, for example, a computer including a processor (microprocessor), memory, communication interface, and the like. The LNB 203 collects operation data of the mounter 202 and transmits it to the server 204 .

The server 204 stores the operation data of the mounter 202 transmitted from the LNB 203 in the database 205 . The server 204 also stores subjective data as feedback information for the diagnosis result of the mounter 202 transmitted from the LNB 203 in the database 205 .

The mail server 25A receives reports such as the malfunction candidate unit list created by the server 13A. Accordingly, the user who manages or maintains the mounting machine 202 in the factory 201D can receive the malfunction candidate unit and confirm whether the malfunction candidate unit is really malfunctioning in the mounting machine 202. FIG. When the user confirms that the unit is really malfunctioning, the user contacts the person in charge of the sales company 201C, etc., and arranges for the unit (malfunctioning unit) that was confirmed to be malfunctioning to be collected. After maintenance, the unit will be returned.

The input device 206 is a device into which subjective data is input by a user's input. The input device 206 is implemented by, for example, a computer including a processor (microprocessor), memory, communication interface, and the like.

Here, the subjective data indicates the result of confirming whether the malfunction candidate unit is really malfunctioning in the mounter 202, and subjectively determines the validity of the malfunction candidate unit. In this embodiment, subjective data is input for each unit.

11 is a diagram showing an example of the subjective data shown in FIG. 7. FIG.

For example, as shown in FIG. 11, the subjective data includes information indicating the unit serial number that uniquely identifies the unit, the person who determined the malfunction, the date and time of the determination, the result of determining whether the malfunction was successful, and the physical condition. The malfunction judgment person and judgment date and time indicate the name of the person (user) in charge who made the malfunction judgment based on the actual unit indicated by the unit serial number, based on the malfunction candidate unit list, and the date and time. The malfunction success/failure determination result indicates whether or not it was correct that the unit identified by the unit serial number was determined to be a malfunction candidate as a result of checking the actual unit.

The physical condition includes different information depending on whether the item is in bad condition or not. In the case of failure, the actual unit indicated by the unit serial number includes information indicating a) the location of the failure, b) the phenomenon of the failure, and c) other special notes. On the other hand, if there is no malfunction, the actual unit indicated by the unit serial number is a) Findings such as no problem, error occurred but can be used, or there is some accuracy problem but can be used, b ) number of feeds, c) maintenance counter, and d) error rate.

<Sales Company 201C>
The sales company 201C is, for example, a company that sells or maintains the mounter 202 or a plurality of units constituting the mounter 202 . An automatic maintenance device 27A, a data collection device 207, and an input device 208 are physically arranged in the area to which the sales company 201C belongs.

The person in charge of the sales company 201C receives the notification, collects the malfunctioning unit, and returns the unit after maintenance.

The automatic maintenance device 27A performs inspections by measuring various states of the malfunctioning unit, and maintains the malfunctioning unit by repairing or replacing the unit. Note that the automatic maintenance device 27A may perform only inspection among inspection, repair, and replacement, and repair and replacement may be performed by the person in charge of maintenance.

The data collection device 207 is a device that collects maintenance performance data, which is data relating to maintenance performed on a malfunctioning unit. The data collection device 207 may collect a part or all of the maintenance performance data of the malfunctioning unit, if it can be collected from the automatic maintenance device 27A. Further, the data collection device 207 collects maintenance performance data input by the maintenance staff to the input device 208 when the maintenance staff performs maintenance on the malfunctioning unit. The data collection device 207 also stores the collected maintenance performance data in the database 205 . Note that the data collection device 207 is implemented by, for example, a computer including a processor (microprocessor), memory, communication interface, and the like.

The input device 208 is implemented by, for example, a computer including a processor (microprocessor), memory, communication interface, and the like. The input device 208 is a device for inputting a maintenance record by a person in charge of maintenance or the like. The input device 208 transmits the input maintenance performance data to the data collection device 207 as maintenance performance data. Note that subjective data may be input to the input device 208 by a person in charge of the sales company 201C, such as a person in charge of maintenance.

As described above, the maintenance performance data is data that is input when maintenance is performed on each maintenance target unit, and is data related to maintenance performed on the malfunctioning unit. In this embodiment, maintenance record data is also input for each unit. Since the subjective data is as described above, the description here is omitted.

FIG. 12 is a diagram showing an example of the maintenance record data shown in FIG. 7. As shown in FIG.

For example, as shown in FIG. 12, the maintenance performance data includes the unit serial number, date and time of receipt, maintenance person and date and time, pre-maintenance status, maintenance execution items, output results of the automatic maintenance device, and shipping data. It contains information indicating the date and time and the mounter number.

The unit serial number indicates an identification number that uniquely identifies the malfunctioning unit. The reception date and time indicates the date and time when the actual unit indicated by the unit serial number was received as the malfunctioning unit. The name of the person in charge of maintenance and the date and time of maintenance of the unit indicated by the unit serial number are shown. The pre-maintenance state indicates the state of the actual unit indicated by the unit serial number before maintenance is performed (at the time of acceptance). A maintenance execution item indicates an item on which maintenance has been performed, for example, an inspected item and an adjusted item. The output result of the automatic maintenance device indicates maintenance execution items by the automatic maintenance device 27A. The shipping date and time indicates the date and time when the unit indicated by the unit serial number was shipped to be returned to the user. The mounter number is a serial number that uniquely identifies the mounter 202 that constituted the malfunctioning unit.

According to the above embodiment, it is possible to diagnose malfunctions of each of the plurality of units constituting the mounting machine 202 belonging to the factory 201D based on the operation information of the mounting machine 202 belonging to the factory 201D of the one customer. . Also, by using the feedback information for the diagnostic results of each of the plurality of units that configure the mounter 202, it is possible to consider variations in each of the plurality of units that configure the mounter 202 belonging to the factory 201D of the one customer. This makes it possible to more accurately diagnose malfunction of at least one unit that truly requires maintenance among the plurality of units that constitute the mounter 202 .

The equipment diagnosis apparatus 10A of the present embodiment uses a diagnostic model 121 created in advance to perform malfunction diagnosis of a plurality of units constituting the mounting machine 202 from the operation information of the mounting machine 202 to be monitored. The diagnostic model 121 created in advance is created by performing statistical machine learning in advance using operation data of the mounting machine 202 belonging to the factory 201D of the one customer. The diagnostic model 121 created in advance corresponds to a learning model common to a plurality of units constituting the mounter 202 belonging to the factory 201D of the one customer.

Furthermore, the diagnostic model 121 is updated (additionally learned) using a certain amount of accumulated feedback information on the diagnostic results of a plurality of units constituting the mounting machine 202 belonging to the factory 201D of the one customer. As a result, by using the feedback information on the diagnosis results of the plurality of units constituting the mounter 202 belonging to the factory 201D of the one customer, the variation of each of the plurality of units due to the difference in usage etc. is reflected in the diagnosis model 121. be able to. As a result, among the plurality of units constituting the mounting machine 202 belonging to the factory 201D of the one customer, it is possible to more accurately diagnose the malfunction of the unit that truly requires maintenance. Note that the updated diagnostic model 121 is subjected to statistical machine learning using feedback information, such as subjective data and maintenance performance data, for diagnostic results of a plurality of units constituting the mounting machine 202 belonging to the factory 201D of the one customer. created by The updated diagnostic model 121 corresponds to a learning model optimized for each of a plurality of units constituting the mounter 202 belonging to the one customer's factory 201D.

As described above, in the equipment diagnosis system 20A, 1) the operation data of the mounting machine 202 (a plurality of units constituting the customer's equipment) is collected, and 2) the AI, that is, the equipment diagnosis device 10A is determined from the collected operation data. Diagnose the malfunction of the customer's equipment, and 3) transmit the diagnosis result to the customer. In addition, in the equipment diagnosis system 20A, 4) when the customer confirms the transmitted diagnosis result and inputs feedback information as subjective data, 5) the feedback information as subjective data is collected, and AI teaching data is collected. used as In addition, in the equipment diagnosis system 20A, 6) at least one of the plurality of units constituting the mounter 202 diagnosed as malfunctioning is collected by the sales company 201C or the like, and maintenance is performed manually or automatically. 7) Maintenance performance data, which is the result of manual or automatic maintenance, is collected and used as teaching data for AI. By executing the data flow of 1) to 7) for each customer, it becomes possible to more accurately diagnose malfunction of at least one unit that truly requires maintenance.

(Possibility of other embodiments)
Although the equipment diagnosis system of the present invention has been described above in the embodiments, there are no particular restrictions on the subject or device that executes each process. It may also be processed by a processor (discussed below) or the like embedded within a locally located specific device. It may also be processed by a cloud server or the like located at a location different from the local device. Moreover, although the diagnostic model 121 was described as being statistically machine-learned in the embodiment, the present invention is not limited to this. The diagnostic model 121 may be learned using a learning method other than statistical machine learning, such as deep learning.

It should be noted that the present invention is not limited to the above embodiments. For example, another embodiment realized by arbitrarily combining the constituent elements described in this specification or omitting some of the constituent elements may be an embodiment of the present invention. The present invention also includes modifications obtained by making various modifications to the above-described embodiment within the scope of the gist of the present invention, that is, the meaning of the words described in the claims, which a person skilled in the art can think of. be

Moreover, the present invention further includes the following cases.

(1) The above device is specifically a computer system comprising a microprocessor, ROM, RAM, hard disk unit, display unit, keyboard, mouse and the like. A computer program is stored in the RAM or hard disk unit. Each device achieves its function by the microprocessor operating according to the computer program. Here, the computer program is constructed by combining a plurality of instruction codes indicating instructions to the computer in order to achieve a predetermined function.

(2) A part or all of the components constituting the above device may be configured from one system LSI (Large Scale Integration). A system LSI is an ultra-multifunctional LSI manufactured by integrating multiple components on a single chip. Specifically, it is a computer system that includes a microprocessor, ROM, RAM, etc. . A computer program is stored in the RAM. The system LSI achieves its functions by the microprocessor operating according to the computer program.

(3) A part or all of the constituent elements constituting the above devices may be composed of a detachable IC card or a single module. The IC card or module is a computer system composed of a microprocessor, ROM, RAM and the like. The IC card or the module may include the super multifunctional LSI. The IC card or the module achieves its function by the microprocessor operating according to the computer program. This IC card or this module may be tamper resistant.

(4) Further, the present invention may be the method shown above. Moreover, it may be a computer program for realizing these methods by a computer, or it may be a digital signal composed of the computer program.

(5) The present invention also provides a computer-readable recording medium for the computer program or the digital signal, such as a flexible disk, hard disk, CD-ROM, MO, DVD, DVD-ROM, DVD-RAM, BD ( Blu-ray (registered trademark) Disc), semiconductor memory, or the like. Moreover, it may be the digital signal recorded on these recording media.

Further, according to the present invention, the computer program or the digital signal may be transmitted via an electric communication line, a wireless or wired communication line, a network represented by the Internet, data broadcasting, or the like.

The present invention may also be a computer system comprising a microprocessor and memory, the memory storing the computer program, and the microprocessor operating according to the computer program.

Also, by recording the program or the digital signal on the recording medium and transferring it, or by transferring the program or the digital signal via the network or the like, it can be implemented by another independent computer system. You can do it.

INDUSTRIAL APPLICABILITY The present invention can be used for equipment diagnosis systems and equipment diagnosis methods for more appropriately diagnosing malfunctions in manufacturing equipment, and in particular, for properly diagnosing malfunctions in mounters and a plurality of units constituting mounters in the surface mounting industry. It can be used for an equipment diagnosis system and an equipment diagnosis method for

10, 20, 20A equipment diagnosis system 10A equipment diagnosis device 11 operation information acquisition unit 12, 22 malfunction diagnosis unit 13 output unit 13A, 204 server 13B, 25A mail server 14 feedback information acquisition unit 25 equipment recovery instruction unit 26 maintenance work instruction unit 27 maintenance section 27A automatic maintenance device 121 diagnostic model 123 pattern comparison section 124 level determination section 202 mounter 203 LNB
205 database 206, 208 input device 207 data collection device 1211 normal behavior pattern 1212 normal correlation pattern 1213 preprocessing unit

Claims (13)

an operation information acquisition unit that acquires operation information of each of a plurality of manufacturing facilities operated in a customer's factory;
a failure diagnosis unit located in the area of a diagnosis provider that provides facility diagnosis results to the customer's factory, and diagnosing a failure of at least one of the plurality of manufacturing facilities based on the operation information; ,
an equipment recovery instruction unit that instructs a manufacturer that manufactures the one manufacturing equipment or a sales company that sells the one manufacturing equipment for the at least one manufacturing equipment diagnosed as malfunctioning by the malfunction diagnosis unit;
Equipment diagnostic system. moreover,
A maintenance unit that performs maintenance of the at least one manufacturing facility diagnosed as malfunctioning by the malfunction diagnosis unit;
The facility diagnosis system according to claim 1. moreover,
A maintenance work instruction unit that instructs the maintenance unit to perform maintenance of the at least one manufacturing facility diagnosed as malfunctioning by the malfunction diagnosis unit;
The facility diagnosis system according to claim 2. moreover,
further comprising a maintenance work instruction unit that instructs the user to perform maintenance on the at least one manufacturing facility that has been diagnosed as malfunctioning by the malfunction diagnosis unit;
The facility diagnosis system according to claim 1. an output unit that outputs a diagnosis result of the malfunction diagnosis unit;
A feedback information acquisition unit that acquires feedback information for the diagnosis result,
The equipment diagnosis system according to claim 2 or 3 . an output unit that outputs a diagnosis result of the malfunction diagnosis unit;
A feedback information acquisition unit that acquires feedback information for the diagnosis result,
The facility diagnosis system according to claim 4. The feedback information includes at least one of information on the maintenance result of the at least one manufacturing facility by the maintenance department and information on the maintenance result by the confirmer,
The facility diagnosis system according to claim 5. The feedback information includes information on whether or not the malfunction diagnosis by the malfunction diagnosis unit is correct.
The equipment diagnostic system according to any one of claims 5 to 7 . The malfunction diagnosis unit has a diagnosis model,
the diagnostic model is updated based on the feedback information;
The facility diagnosis system according to any one of claims 5-8 . The diagnostic model is created by learning using operation record data including operation information of each of the plurality of manufacturing equipment,
The facility diagnosis system according to claim 9 . The manufacturing equipment includes a surface mount device or at least one element of a plurality of elements that constitute the surface mount device,
The facility diagnosis system according to any one of claims 1-10 . the diagnostic model is one of a plurality of diagnostic models;
wherein the plurality of diagnostic models are provided corresponding to a plurality of customers;
The facility diagnosis system according to claim 9 . Acquire the operation information of each of the multiple manufacturing facilities operated at the customer's factory,
Based on the obtained operation information, a malfunction diagnosis unit located in the area of a diagnosis provider that provides the customer's factory with the result of equipment diagnosis diagnoses a malfunction of at least one of the plurality of manufacturing equipment. diagnose and
instructing a manufacturer that manufactures the one manufacturing equipment or a sales company that sells the one manufacturing equipment for the at least one manufacturing equipment that has been diagnosed as malfunctioning by the malfunction diagnosis unit;
Equipment diagnostic method.

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