JP2010027076A - Equipment diagnostic method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an equipment diagnostic method for enabling a user side to analyze raw information with a large amount of information without transmitting the raw information to an equipment diagnostic center side. <P>SOLUTION: When an advanced analysis diagnostic part 6 performs diagnosis on the basis of processing information which is processed and output by an equipment monitoring part 5, a prescribed equipment management data processing program is extracted from an equipment management data processing program group 12 provided in the advanced analysis diagnostic part 6 when the diagnosis has to be additionally performed, and the advanced analysis diagnostic part 6 uploads the prescribed equipment management data processing program to the equipment monitoring part 5 through a communication network 10 to repeat the diagnosis necessary times. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

The present invention relates to a method for managing and diagnosing the state of equipment.

  Conventionally, management of equipment in general companies such as factories has been accompanied by abnormalities in the vibration, sound, pressure, temperature, etc. of equipment, while the person who manages the equipment always watches the operating status of equipment. If necessary, monitor the condition of the relevant equipment at the site, stop the operation of the equipment in some cases, investigate the cause, repair it urgently, replace the parts, or make a more serious situation If this is the case, it is common to inquire with the equipment manufacturer and consider countermeasures.

JP-A-1-184599 JP-A-11-252670 JP-A-11-102303 Japanese Unexamined Patent Publication No. 63-25740

  However, in the above-mentioned conventional example, since the judgment is made based on personal knowledge and experience within the range that can be handled by the person who manages the equipment, the judgment result is not necessarily accurate, and in some cases it is erroneous. In some cases, the judgment result is not objective.

  On the other hand, in recent years, the total cost of the factory has been reduced by improving the accuracy of equipment management to improve the safe operation and productivity of the factory, increasing the efficiency of specialist engineers for equipment management, and further improving the reliability of equipment. There is an increasing demand for reduction. In addition, there is a growing momentum that it is necessary to thoroughly carry out predictive diagnosis and routine maintenance plans for each equipment, especially important equipment.

  For example, in a factory where a large number of rotating devices and the like are installed, a person who manages facility equipment regularly performs maintenance work while performing daily operation work. However, in order to solve problems related to predictive maintenance, such as equipment diagnosis and predictive diagnosis of rotating equipment, grasping of optimal operating conditions or remaining life of rotating equipment, specialized knowledge is required, so the equipment is managed. There is a limit to the response by the person alone.

  Therefore, it is desirable to have a specialist engineer having such specialized knowledge resident in the factory, but it is often difficult in terms of the cost of factory operation, such as improving the efficiency of personnel.

The present invention solves the above-mentioned problems, the purpose of which is to collect and collect various data by grasping and managing the minimum operating state of equipment and equipment by continuous measurement by the device, or by measurement by humans, When information corresponding to the level of abnormality is extracted from the collected information, the information is promptly transmitted to the facility diagnosis center, which is a specialized technical group. Advanced analysis and diagnosis processing is performed, and the best information for equipment that is determined to be abnormal is promptly notified to the equipment management side, and further, the equipment management is managed from the advanced analysis diagnosis section to the equipment monitoring section on the user side. It provides equipment diagnosis method analysis can be on the user side without transmitting more raw information amount of information by uploading a data processing program in the facility diagnosis center side It is an do.

Equipment diagnostic method according to the present invention for achieving the above object, the equipment state detection information detected by the equipment state detection means attached to the equipment to the signal processing by the facility management data processing section facility state determining section levels determined with respect to control reference value by said collected by facility monitoring unit related information of the determined equipment as abnormal when it is determined from the facility state determining unit level determination to abnormal, treated treated Information is sent to the advanced analysis and diagnosis unit via the communication network, and the processing information collected, processed and output by the facility monitoring unit is subjected to advanced analysis by the advanced analysis and diagnosis unit and the cause of the abnormality of the corresponding equipment It identifies the improvement measures, a facility equipment diagnostic method for transmitting the result of the specific to the facility monitoring unit, the altitude based on the previous SL facility monitoring unit in the processing and the processing information output Communications network by extracting a predetermined facility management data processing program from the facility management data processing programs provided in the high degree of analysis and diagnosis unit when it is necessary to add to diagnose Upon diagnosing in analysis diagnosis unit And uploading from the advanced analysis and diagnosis unit to the equipment monitoring unit, and repeating the above steps as many times as necessary .

  Since the present invention has the configuration and operation as described above, the person in charge in the factory on the user side grasps and manages the operation status of the equipment on a daily basis, collects various data, and collects the data from the collected information. When information corresponding to the level of abnormality is extracted, the information is promptly transmitted to the facility diagnosis center side, which is a specialized technical group, using a communication network such as a network, the Internet, or a public line, and the facility diagnosis center side In the advanced analysis and diagnosis section, the information can be advanced analyzed and the best information on the equipment judged to be abnormal can be quickly returned to the user side. The user side is instructed by a specialized technical group based on that information. Appropriate responses can be implemented quickly.

  In addition, the management of equipment and equipment that has been done based on the knowledge and judgment of the person in charge of the work has traditionally been entrusted to the equipment diagnosis center, which consists of expert engineers with specialized knowledge. There is no need to reside in individual factories, and the safety and productivity of equipment on the user's side will be improved, the accuracy of equipment management will be improved, personnel efficiency will be improved, and the total cost of the factory will be reduced. Can be planned.

In addition, when the advanced analysis diagnosis unit determines that the accuracy of the diagnosis is improved by performing the advanced analysis again, as a secondary process, the facility monitoring on the user side is further monitored from the advanced analysis diagnosis unit on the facility diagnosis center side. The equipment management data processing program is uploaded to the department via the communication network, and the advanced analysis is performed again on the user side.

Equipment management data processing program is uploaded to the user side, and advanced analysis is performed again on the user side, so that a huge amount of raw data such as equipment state detection information detected by the equipment state detection means is received on the equipment diagnosis center side The information transfer burden on the communication network is reduced because only a small amount of information with only analysis results is sent to the facility diagnosis center.

  Moreover, since the raw data does not travel on the communication network, the security of information is improved.

It is a block diagram showing the configuration of settings備機instrument diagnostic system. It is a block diagram showing a user-side configuration of the settings備機instrument diagnostic system. It is a figure which shows the structural example of the communication network between an equipment monitoring part and an advanced analysis diagnostic part. It is a block diagram which shows the structure of an equipment state detection means and an advanced analysis diagnostic part. It is a block diagram which shows the structure of an advanced analysis diagnostic part. It is a figure which shows the detailed structure of an advanced data analysis part. It is a diagram illustrating a facility management data processing program group related to the signal processing recipe. It is a figure which shows an example of the display screen of an equipment monitoring part. It is a figure which shows an example of the display screen of an equipment monitoring part. It is a figure which shows an example of the diagnostic result list | wrist sent from an advanced analysis diagnostic part. It is a figure which shows an example of the primary diagnostic result sent from an advanced analysis diagnostic part. It is a figure which shows an example of the secondary diagnosis result sent from an advanced analysis diagnostic part. It is a figure which shows another example of the diagnostic result sent from an advanced analysis diagnostic part. It is a figure which shows the relationship between the cause diagnosed by an advanced analysis diagnostic part, and a result. It is a figure which shows the output example secondary-processed using the program for the equipment management data processing uploaded to the user side. It is a figure which shows an example of the rolling bearing abnormal vibration criteria.

An embodiment of the equipment diagnosis method according to the present invention will be specifically described with reference to the drawings. Figure 1 is a block diagram showing the configuration of settings備機unit diagnosis system, Figure 2 is a block diagram showing a user-side configuration of the settings備機unit diagnostic system, the communication network between the 3 facility monitoring unit and advanced analysis and diagnosis section FIG. 4 is a block diagram showing the configuration of the equipment state detection means and the altitude analysis diagnosis unit, FIG. 5 is a block diagram showing the configuration of the altitude analysis diagnosis unit, and FIG. 6 is a detailed diagram of the altitude data analysis unit. FIG. 7 is a diagram illustrating the configuration, and FIG. 7 is a diagram illustrating the relationship between the signal processing recipe and the facility management data processing program group.

8 and 9 are diagrams showing examples of the display screen of the equipment monitoring unit, FIG. 10 is a diagram showing an example of a list of diagnosis results sent from the advanced analysis diagnostic unit, and FIG. 11 is sent from the advanced analysis diagnostic unit 1 FIG. 12 is a diagram showing an example of the secondary diagnosis result, FIG. 12 is a diagram showing an example of the secondary diagnosis result sent from the advanced analysis diagnostic unit, FIG. 13 is a diagram showing another example of the diagnostic result sent from the advanced analysis diagnostic unit, 14 is a diagram showing a relationship between causes and results diagnosed by the advanced analysis and diagnosis unit, FIG. 15 is a diagram showing an example of output subjected to secondary processing using the facility management data processing program uploaded to the user side, and FIG. These are figures which show an example of the rolling bearing abnormal vibration criteria.

  1 to 7, A is a factory on the user side B where a large number of equipment such as rotating equipment is installed, and C is a facility equipment located at a distance from the user side B. The facility diagnostic center has a specialized technical group that is familiar with diagnostic work.

  In the factory A, a rotating device such as a fan 1a and a pump 1b and a large number of facility devices 1 that perform various functions are installed. Various facility devices 1 include facilities for detecting the state of the facility device 1. Equipment state detectors 2a and 2b composed of various sensor elements or the like serving as state detecting means are attached.

  From the equipment state detectors 2a and 2b, daily equipment state detection information related to the equipment device 1 is transmitted to the equipment management data processing unit 3, and the equipment state information signal-processed by the equipment management data processing unit 3 is the equipment state determination unit. 4 is transmitted.

  The equipment state determination unit 4 determines the level of the information output from the equipment management data processing unit 3 with respect to a preset management reference value and outputs the information. The facility management data processing unit 3 and the facility state determination unit 4 are installed at a site around the facility device 1 in the factory A.

  For example, the raw vibration data is collected by the equipment state detectors 2a and 2b such as vibration sensors provided in the equipment 1 and the raw vibration data is filtered, integrated, and averaged by the equipment management data processing unit 3. , O / A value (output of averaging process) indicating the state of the equipment 1 obtained as a result of signal processing by the equipment state determination unit 4 after signal processing such as peak detection processing, and the value of equipment state parameters such as peak value Is compared with a preset threshold value to primarily determine the state of the equipment device 1.

  The information determined by the equipment state determination unit 4 is transmitted to the equipment monitoring unit 5 that collectively manages the equipment 1 of the entire factory A, and the equipment monitoring unit 5 determines the level from the equipment state determination unit 4 and outputs the equipment. Collects, processes, outputs, and saves related information of the device 1. That is, the equipment monitoring unit 5 stores equipment state parameters as trend management data and collects and manages equipment related information such as specifications and history of the equipment 1.

  The facility monitoring unit 5 on the user side B and the advanced analysis diagnosis unit 6 on the facility diagnosis center side C are configured to be able to communicate with each other via a communication network 10 such as a network, the Internet, or a public line. 5, information on the relevant equipment 1 determined to be abnormal by the equipment state determination unit 4 is collected, processed, and transmitted to the equipment diagnosis center C via the communication network 10.

  That is, when it is determined that the equipment state determination unit 4 is abnormal, the trend management data collected and managed by the equipment monitoring unit 5 together with the value of the equipment state parameter that is the primary processing result, the specifications of the equipment 1 and the history, etc. Is transmitted to the facility diagnosis center side C via the communication network 10.

  Information transmitted from the equipment monitoring unit 5 on the user side B is received by the altitude analysis diagnosing unit 6 on the equipment diagnosis center side C, and the information output from the equipment monitoring unit 5 is automatically analyzed in the altitude analysis diagnosing unit 6. Then, the cause of the abnormality of the corresponding equipment 1 and the improvement measure are specified, and the specified result is transmitted to the equipment monitoring unit 5.

  As shown in FIG. 4, the advanced analysis / diagnostic unit 6 evaluates the diagnostic information sent from the user side B and automatically diagnoses the abnormal part, the cause of the abnormality, the remaining life, the countermeasure (improvement method), etc. An automatic diagnosis unit 6a is provided.

  Further, as shown in FIGS. 5 and 6, the altitude analysis / diagnostic unit 6 arbitrarily decomposes the raw waveform signals detected by the equipment state detectors 2a and 2b into wavelets. Analysis by analysis technology, SDP (Symmetrized Dot Patterns) analysis by visual analysis technology that plots raw waveform signals detected by equipment state detectors 2a and 2b on target coordinates, and further dimensions such as vibration and sound Non-dimensional feature parameters are used to characterize signal features, etc., thereby detecting anomalies. Dimensionless symptom parameters, and analysis of pursuing causes by using multiple cross-correlated signals. An advanced data analysis unit 6b is provided that increases the accuracy of abnormality detection using advanced data analysis such as multivariate analysis, which is a technique, and sends the analysis result to the automatic diagnosis unit 6a.

The advanced data analysis unit 6b is provided with the signal processing recipes of the SDP file 11a, wavelet file 11b, FFT file 11c, dimensionless sign parameter file 11d, multivariate analysis file 11e, and other analysis file 11f. If the diagnosis is made by the advanced analysis diagnosis unit 6 based on the information output from the facility monitoring unit 5 and additional diagnosis is required, the facility management data processing program group shown in FIGS. 6 and 7 is used. A predetermined facility management data processing program is extracted from 12, and in each signal processing recipe SDP file 11a, wavelet file 11b, FFT file 11c, dimensionless signs parameter file 11d, multivariate analysis file 11e, and other analysis files 11f From the advanced analysis diagnostic unit 6 on the facility diagnosis center side C to the equipment on the user side B It is uploaded to the visual part 5.

  As shown in FIGS. 4 and 5, the advanced analysis / diagnostic unit 6 analyzes the change tendency from the temporal change data sent from the equipment state determination unit 4 on the user side B, and the analysis result is the automatic diagnosis unit 6a. To be sent to. In addition, the output sent to the automatic diagnosis unit 6a is sent to the advanced analysis diagnosis unit 6 to the life prediction unit, and the trend management unit 6c in which the life prediction analysis is performed and the change over time managed by the trend management unit 6c Life prediction is performed based on the data, and the life prediction is calculated by a unique calculation formula obtained from the past diagnosis results, and a life prediction unit 6d for sending the analysis result to the automatic diagnosis unit 6a is provided.

  The advanced analysis / diagnostic unit 6 detects a characteristic frequency from precision diagnosis information by fast Fourier transform (FFT), which is a typical frequency analysis technique, and compares it with the precision diagnosis information at normal time. And a precision diagnosis unit 6e is provided for sending the analysis result to the automatic diagnosis unit 6a.

  The advanced analysis / diagnostic unit 6 selects an optimum improvement method according to the specifications of the equipment 1 and the contents of the diagnosis from the improvement method database constructed based on past diagnosis and improvement implementation, and automatically outputs the result. The improvement method selection unit 6f used as the diagnosis result of the diagnosis unit 6a, the specifications, maintenance plan, maintenance results, etc. of the equipment device 1 on the user side B are managed, and automatic analysis is performed based on this information, and concrete measures are taken. And a maintenance information part 6g that contributes to improvement methods and the like.

  The equipment monitoring unit 5 on the user side B and the advanced analysis diagnostic unit 6 on the equipment diagnosis center side C are connected by a communication network 10 such as a network, the Internet, a public line, etc., and the equipment state detection exchanged between them. Various information such as information and diagnosis reports are converted into electronic files and transmitted / received by e-mail or the like.

  3 in FIG. 3 is a firewall installed between the external network and the internal network, and prevents unauthorized third party unauthorized intrusion and data leakage, alteration, destruction, etc. from the outside. Is for. If security is ensured, a configuration without the firewall 9 may be used.

  Note that a dedicated line, a communication satellite, or the like may be used as another communication network 10 that connects the facility monitoring unit 5 on the user side B and the altitude analysis diagnosis unit 6 on the facility diagnosis center side C.

  The information sent from the equipment monitoring unit 5 on the user side B is subjected to altitude analysis in the altitude analysis / diagnostic unit 6, and the result is returned to the equipment monitoring unit 5 on the user side B. The best treatment is notified to the corresponding equipment 1 determined to be capable of being dealt with immediately.

  As equipment state detection means attached to a large number of equipment 1, for example, as shown in FIG. 4, as a rotating machine vibration diagnosis, an online device or a portable equipment diagnostic measuring instrument is adopted as a standard diagnosis or a precision diagnosis. As an oil diagnosis, an oil diagnostic device is employed.

  Here, the standard diagnosis is to judge whether the equipment is normal or abnormal from the vibration level of the equipment and changes over time, and to easily perform the cause, part, degree, life prediction, etc. Is a detailed analysis by frequency analysis etc. of events that cannot be judged by standard diagnosis.

  In addition, as equipment status detection means in pipe management, for example, nondestructive inspection using UT (Ultra Sonic), corrosion diagnosis by an infrared camera, etc. are performed, and equipment status detection means in tank bottom plate diagnosis are as follows: For example, the tank bottom plate whole surface nondestructive inspection using UT is implemented, and as a facility state detection means in general stationary equipment, for example, nondestructive inspection using UT or corrosion diagnosis by an infrared camera is performed.

  In addition, the daily inspection system shown in the upper right of Fig. 4 is implemented by inputting the inspection information of the plant that the operator conducts daily into the mobile terminal at the time of on-site inspection, and realizes data management with a personal computer It mainly deals with five senses information such as process information (temperature and pressure during operation), leakage around equipment, and abnormal noise.

  The equipment state detectors 2a and 2b comprising sensor elements attached to the equipment 1 always detect various state conditions such as vibration, temperature, pressure, lubricating oil component, sound, current, voltage and the like. There is a portable equipment diagnostic measuring instrument that measures various information with a portable measuring instrument when an operator goes around the equipment equipment 1 without directly attaching these equipment condition detectors 2a, 2b to the equipment equipment 1. There may be.

  For example, an example in which the equipment 1 is a rotating equipment will be described in detail. Equipment status detection information of the rotating equipment is equipment management from equipment status detectors 2a and 2b attached to a number of rotating equipment in the factory A. It is transmitted to the data processing unit 3.

  The equipment management data processing unit 3 performs signal processing such as filtering and speed conversion on the signal received as the primary processing, and further performs peak processing and frequency analysis to diagnose the vibration status of the corresponding rotating equipment. A determination signal composed of an acceleration overall value, an acceleration peak value, a speed overall value, and the like necessary for the output is output (primary processing output) and transmitted to the equipment state determination unit 4.

  A management reference value prepared in advance by the equipment diagnosis center side C is input to the equipment state determination unit 4, and a determination signal that is information output from the equipment management data processing unit 3 is compared with the management reference value. Determine the level of the corresponding rotating device. Level determination is generally divided into “normal” and “abnormal”, and “abnormal” is further classified into “caution” and “danger”. The determined determination signal is recorded in the equipment monitoring unit 5.

  In the equipment monitoring unit 5, when the equipment state determination unit 4 determines “Caution” or “Danger” indicating “abnormal”, the measurement data of the corresponding rotating device, the history data, and the corresponding rotating device Record predetermined information such as measurement data of rotating equipment currently operating in different locations with the same model, and the determination signal (primary processing output result) transmitted from the equipment state determination unit 4 Are automatically sent to the advanced analysis / diagnostic unit 6 via a communication network 10 such as a network, the Internet, or a public line. In the case of “normal”, the mail is automatically transmitted periodically (for example, once a day).

  In addition, the system which downloads data from the equipment monitoring part 5 using the monitoring screen of a homepage form from the equipment diagnostic center side C may be used.

  That is, in the present embodiment, the equipment monitoring unit 5 attaches and outputs abnormal data in the case of an abnormality in addition to the presence / absence of an abnormality, and sends it to the advanced analysis / diagnostic unit 6.

  The equipment state determination unit 4 has a function of checking whether or not the information determined as “abnormal” is a transient phenomenon due to a disturbance, thereby causing the transient phenomenon. Only information determined to be “abnormal” due to a cause other than the above is transmitted to the advanced analysis diagnosis unit 6.

  8 and 9 are examples of images displayed on the equipment monitoring unit 5 on the user side B and sent to the altitude analysis and diagnosis unit 6. FIG. 8 shows a list of measurement data related to the corresponding rotating equipment, and FIG. It is a graph which shows a time-dependent change of the measurement point of a specific rotation apparatus among rotation apparatuses to perform.

  In the determination column 7a shown in FIG. 8, the distinction between normal “◯”, caution “Δ”, and danger “×” is recorded. Further, in the time-dependent change graph shown in FIG. 9, the vertical axis indicates the vibration value (mm / sec), the horizontal axis indicates the date, and the channel numbers 7c of “1” to “32” in the selection column 7b on the screen of FIG. By selecting the period type 7d and clicking the graph display button 7e, a time-dependent change graph shown in FIG. 9 is displayed.

  In the determination column 7a of FIG. 8, only information about the rotating device determined as caution “Δ” and danger “×” is sent from the equipment monitoring unit 5 to the altitude analysis diagnosis unit 6 for altitude analysis. When various pieces of information related to the rotating equipment shown in FIGS. 8 and 9 are sent from the equipment monitoring unit 5 to the advanced analysis / diagnostic unit 6, the advanced analysis / diagnostic unit 6 displays the notice “Δ” shown in the determination column 7a of FIG. The information on the rotating equipment determined as dangerous “x” is subjected to advanced analysis.

The advanced analysis / diagnostic unit 6 performs advanced analysis on the information about rotating equipment that has been transmitted as a notice “△” and a danger “×”, and necessary information such as causes, optimum countermeasures, and future maintenance plans are required. The items are extracted and specified, and they are sent back to the equipment monitoring unit 5 in the factory A on the user side B by e-mail via a communication network 10 such as a network, the Internet, or a public line .

  That is, after performing an advanced analysis on a rotating device having nine measurement points “10” and “13” to “20” with the channel numbers of FIG. 8, the diagnosis result shown in FIG. It is returned to the equipment monitoring unit 5.

  FIG. 10 is an example of an image sent from the advanced analysis diagnostic unit 6 relating to an extruder which is an example of a rotating device. When the “Le” mark described in the primary diagnosis result column 8a is selected and clicked, FIG. A primary diagnosis result in which “Cause” and “Countermeasure” as illustrated are commented by sentences is attached.

  As shown in FIG. 11, the diagnosis result is a definite expression that avoids expressions that may make a judgment on the user side B as much as possible, and that can take immediate countermeasures.

  When the advanced analysis / diagnostic unit 6 determines that it is necessary to analyze and analyze the information about the rotating equipment determined as the caution “△” and the danger “×” at a high level, The equipment monitoring unit 5 is requested to extract necessary information, the new information transmitted from the equipment monitoring unit 5 is additionally analyzed, and the same analysis as shown in FIG. The result is returned to the equipment monitoring unit 5.

  Although not shown in FIG. 10, when the “L” mark described in the secondary diagnosis result column 8b is selected in the same manner as the primary diagnosis result column 8a and clicked, “instruction” and “ A secondary diagnosis result in which “determination” is commented by a sentence is attached.

  If the corresponding rotating device is at the “danger” level or higher, the altitude analysis diagnosis unit 6 determines “emergency stop”, and in the determination field 8c shown in FIG. The emergency stop “*” determination is recorded in the rotating equipment corresponding to the above and is sent back to the equipment monitoring unit 5, and the operation of the relevant rotating equipment in the factory A on the user side B is emergency stopped. More detailed information is transmitted to the equipment monitoring unit 5 for the rotating device determined as the emergency stop “*”.

  The equipment monitoring section 5 on the user side B and the advanced analysis diagnosis section 6 on the equipment diagnosis center side C are connected by a communication network 10 such as a network, the Internet, or a public telephone, so that two-way communication is possible. Therefore, the user side B can ask the facility diagnosis center side C for explanation by e-mail, telephone or the like until the diagnosis result of the rotating device is satisfactory.

  Next, the configuration of the advanced analysis diagnosis unit 6 will be described in detail. The advanced analysis and diagnosis unit 6 has various equipments 1 arranged in a multi-factory factory A for each equipment 1 and the standards and dimensions of various equipment and parts constituting the equipment 1, its manufacturer, Manufacturing date, equipment specification information on various specification items (for example, rotation speed, shaft diameter, operating temperature, etc.), maintenance history information such as installation date, operating history, repair history, and equipment up to now Measurement value history information obtained when 1 is inspected and diagnosed is recorded and accumulated.

  Furthermore, the various types of equipment 1 are classified according to the size, load state, installation environment, etc. of the equipment 1 and the vibration state and remaining life of the equipment 1 under the optimum operating conditions are calculated statistically and theoretically. The data, the cause of the abnormality and the countermeasures against the abnormal phenomenon that occurred in the past are systematically organized, recorded and accumulated.

  For example, when the altitude analysis diagnosis unit 6 diagnoses the vibration state of a predetermined rotating device, the altitude analysis diagnosis unit 6 includes the rotation speed, shaft diameter, and load state of the population of the rotating device in the field to which the rotating device belongs. Since various kinds of information such as the lubrication state and the installation state have already been input, it is possible to grasp the proper vibration state of the rotating device population.

  Then, the numerical value of these appropriate vibration states is adopted as a management reference value, and the level of the vibration state of the predetermined rotating device is determined by comparing with the measured value of the vibration state of the target rotating device. I can do it.

  For example, in the rolling bearing abnormal vibration determination standard shown in FIG. 16, the horizontal axis is the DN value (shaft diameter × rotational speed) and the vertical axis is the vibration acceleration value. If the DN value is known, the normal, caution, danger, etc. graphs become the respective management thresholds when the position is viewed in the upward direction on the vertical axis. This standard is created from the result of organizing and constructing the diagnosis result data.

  Furthermore, by accumulating the maintenance history information and measurement value history information of the population of rotating equipment, it is possible to grasp the cause of abnormality of a given rotating equipment (for example, structural abnormal condition, bearing abnormal condition, etc.) In this case, it is possible to present the best countermeasure for what kind of treatment should be performed (improvement method database 6f1 in FIG. 5).

  Similarly, it is possible to estimate how long the current state will continue, and in other words, to estimate the remaining life of a predetermined rotating device (remaining life determination database 6d1 in FIG. 5).

  These can be calculated statistically and theoretically based on the facility equipment specification information, maintenance history information, measurement value history information, etc. of the rotating equipment population accumulated in the advanced analysis diagnosis unit 6.

  In general, assuming that equipment 1 manufactured with the same material and the same specifications is operated under the same conditions, the equipment 1 naturally has the same history. However, since the actual equipment 1 can hardly be exactly the same, the causal relationship of the equipment 1 determined as “abnormal” is very diverse.

  Therefore, a lot of data obtained from the actual equipment 1 is classified according to the size, load state, installation environment, etc. of the equipment 1 based on the failure physics theory and statistical theory, and these information are systematically Organizing and accumulating and diagnosing the current equipment state of the target equipment 1 by comparing the data indicating the current state of the target equipment 1 and the accumulated population information. Yes (automatic diagnosis unit 6a).

  Furthermore, if the equipment 1 continues to operate in its current state, what kind of state will it progress to, or what kind of measures should be taken in advance to prevent it when it progresses to an abnormal state? It can be inferred whether it is good, and as a result, an effective preventive maintenance measure can be constructed (life prediction unit 6d, improvement method selection unit 6f).

  That is, in the advanced analysis / diagnosis unit 6, all kinds of information and data are recorded and stored for various types of equipment 1 arranged in many factories A. Generally, the information and data are based on the information and data. By assembling a theoretical formula so that a correct tendency can be calculated and comparing the result of this theoretical formula with the actual state of the equipment 1 to improve the accuracy of the theoretical formula while sequentially correcting the coefficient of the theoretical formula In addition to facility diagnosis of various equipment 1, it is possible to construct predictive maintenance of various equipment 1.

  Next, a specific example for deriving the diagnosis result of the equipment 1 in the advanced analysis diagnosis unit 6 will be described with reference to FIG. FIG. 14 shows a part of the diagnostic knowledge matrix table stored in the advanced analysis diagnostic unit 6 in order to derive the diagnosis result of a predetermined blower body. Naturally, the structure of the diagnostic knowledge matrix table is classified according to the model (for example, a fan or a compressor) constituting each equipment 1 and is different.

  The horizontal axis of the diagnostic knowledge matrix table shown in FIG. 14 classifies abnormal phenomena that can occur over a number of items, and the vertical axis shows when the abnormality occurs, the location where the abnormality occurred, the abnormal mode, and the abnormal The change over time, the component configuration of the equipment 1 and the like are configured by a number of items, and the mark “●” is given to the corresponding item.

  These markings are attached not only statistically and empirically, but also based on the theoretical calculation described above. Then, as described above, when information on a predetermined blower body is transmitted from the equipment monitoring unit 5 to the advanced analysis / diagnostic unit 6, marking is automatically given to the items on the vertical axis in the diagnostic knowledge matrix table of FIG. Is done.

  Then, the result of the marking is compared, compared, and calculated with the diagnosis knowledge matrix table as the population of the blower main body already constructed in the advanced analysis diagnosis unit 6 and the information of the predetermined blower main body. A diagnosis result of a predetermined blower body similar to that shown in FIG. 10 is derived.

  In addition, for each of the many abnormal phenomena that may occur in the diagnostic knowledge matrix table, the sentence knowledge that documents the cause of the abnormality, countermeasures, maintenance plan, etc. is linked to the diagnostic knowledge matrix table. Since it is constructed, the diagnosis result as a comment on the predetermined blower body similar to that shown in FIGS. 11 and 12 is written by comparing, comparing, and calculating the information on the predetermined blower body and the diagnosis knowledge matrix table. It is automatically combined and synthesized by knowledge.

  However, in the above configuration, the facility management data processing unit 3 can perform only signal processing prepared in advance, and therefore, only the facility state parameter that is the processing result can be used for diagnosis. Further, when there is effective signal processing (equipment state parameter) for a certain phenomenon, it is necessary to incorporate the function into the equipment management data processing unit 3 installed on the user side B every time, and maintenance and update are troublesome. The problem also arises. In addition, all assumed signal processing can be incorporated in the facility management data processing unit 3 in advance, but it is not efficient and uneconomical to incorporate all functions that are less frequently used.

Therefore, in this embodiment, the output signals of the equipment state detectors 2a and 2b are processed by the equipment management data processing unit 3 and are not immediately judged by the equipment state judgment unit 4, but the output signals of the equipment state detectors 2a and 2b. When it is necessary to perform various advanced arithmetic processing on the raw waveform of the waveform and guide it to the equipment state determination unit 4, the advanced data analysis unit 6b is equipped with a program for equipment management data processing necessary for advanced analysis processing in advance. The facility management data processing program is uploaded to the user side B by remote processing from the facility diagnosis center side C, and the output signals of the facility state detectors 2a and 2b to be processed are sent to the network, Internet or public line. The processing is performed on the user side B via the communication network 10 and the like, and the processing result is sent back to the equipment diagnosis center side C so that the network, Internet or public It can reduce the transmission load of the communication network 10 of the line, etc., together is obtained by solving the problems described above.

That is, when the advanced analysis / diagnostic unit 6 determines that an advanced analysis is necessary again from another point of view, as shown in FIGS. 6 and 7, an instruction for generating a secondary processing program is sent from the automatic diagnosis unit 6a to the advanced data. The necessary equipment management data processing program is selected and extracted from the equipment management data processing program group 12 as appropriate, extracted from the equipment management data processing program group 12, and each equipment management data processing program for secondary processing is converted into an electronic file. Generated.

  6 and 7 show an example in the case of creating the FFT file 11c from the signal processing recipe. When performing advanced analysis by Fast Fourier Transform (FFT), which is a typical frequency analysis technique, first, after averaging processing, waveform cutting processing by a window function is performed, and then Execute the analysis process.

The facility management data processing program group 12 includes various averaging processing programs 12a such as time average processing (Average) and RMS (square root of variance), and various window functions (Time Windows) programs such as Hanning window and Hamming window. 12b, various transform processing programs such as Fourier transform, wavelet (Wavelet), etc. are aggregated for each program according to various functions, and further necessary conditions for operating various programs, program output formats, etc. Is stored.

The facility management data processing program group 12 stores the data itself and an object (small program having each function) in which the processing for handling the data is integrated. Secondary processing is performed from the automatic diagnosis unit 6a. In response to the instruction to generate the program, the advanced data analysis unit 6b selects and combines the optimal objects based on the contents of the signal processing recipe 11, and automatically generates a secondary processing program. Further, each object executes processing possessed by the object, updates and references data, and further exchanges messages with objects having other functions, thereby linking the objects.

  Inter-object exchanges can be processed in a distributed manner because the object itself does not need to know where each other's object exists. As a result, an environment for uploading the necessary analysis processing to the user side B in a timely manner can be realized by converting the signal processing into an object and combining the objects according to the purpose.

  Averaging processing is a random waveform that is composed of a distortion waveform that includes harmonics in addition to the basic waveform, and a frequency component that has multiple periods in addition to the basic waveform, and is used to represent the amplitude level of these waveforms. It is processing to do. The time average process (Average) and RMS (square root of variance) are each expressed by the following equation (1).

  Further, when performing the digital Fourier transform, the periodicity becomes a problem at the joint of the waveform. For example, when analyzing the spectrum of a certain waveform, the high-frequency component generated from the joint of the period is also analyzed, and this high-frequency is not a component in the original waveform, so When the waveform is multiplied by a function that gently attenuates both ends, and this is digital Fourier transformed, a spectrum with the high frequency generated from the joint removed can be observed. A function that gently attenuates both ends for waveform cutting used for such a purpose is called a window function (Time Windows).

  Various window functions (Time Windows) have been devised, depending on the purpose of analysis and the difference in waveform characteristics. Typical examples of the window function are a humming window (suitable for analysis of those whose frequency components are close) and a hanning window (suitable for analysis of those whose waveform components are not so close).

  The Fourier Transform is a typical frequency analysis that transforms a complex signal into a collection of many sine waves, and a wavelet is an arbitrary wavelet (wavelet). Is a time-frequency analysis.

In response to an instruction for generating a secondary processing program from the automatic diagnosis unit 6a, the advanced data analysis unit 6b selects and combines various optimum programs from the facility management data processing program group 12.

  For example, when FFT is selected as the secondary processing, the time average processing (Average) is selected from the averaging processing program 12a according to the variation form and frequency band of the target signal when generating the FFT file 11c. Then, the Hamming window is selected from the window function program 12b, the Fourier transform is selected from the analysis processing program 12c, and further, the relationship definition, connection, necessary conditions for the program to operate, the output format of the program, etc. The secondary processing program is generated by being matched and converted into an electronic file (11c ').

An electronic file 11c 'storing a facility management data processing program, which is a secondary processing program generated by the advanced data analysis unit 6b of the facility diagnosis center side C, is sent to the user side via the communication network 10 as shown in FIG. The equipment monitoring unit 5 uploads the data to the equipment management unit 5, and the equipment monitoring unit 5 sends a predetermined equipment management data processing program from the uploaded electronic file 11c 'to the equipment management data processing unit 3.

The equipment management data processing unit 3 processes and outputs equipment state detection information detected by the equipment state detectors 2a and 2b serving as equipment state detection means by a predetermined equipment management data processing program sent to the equipment management data processing unit 3. The information output from the management data processing unit 3 is level-determined by the equipment state determination unit 4 with respect to the management reference value and is output. Are collected, processed, and output to the advanced analysis diagnosis unit 6 on the facility diagnosis center side C via the communication network 10.

FIG. 15 shows a case where secondary processing (advanced analysis) is performed in the facility management data processing unit 3 by the FFT facility management data processing program uploaded to the user side B with respect to the measurement point of the channel number “13” in FIG. An output example of a program processing result on the user side B is shown. The analysis result shown in FIG. 15 is also sent to the equipment diagnosis center side C via the communication network 10 and further analyzed by the advanced analysis diagnosis unit 6 on the equipment diagnosis center side C.

Then, the altitude analysis / diagnostic unit 6 that has received the information output from the facility monitoring unit 5 further analyzes the altitude to identify the cause of the abnormality of the corresponding facility device 1 and the countermeasure to improve it, and the identification result is transmitted to the communication network. 10 is transmitted to the equipment monitoring unit 5 via 10 and the process of uploading the equipment management data processing program is further repeated to the equipment monitoring unit 5 as necessary.

  Further, the facility monitoring unit 5 includes a maintenance information database 13 serving as a maintenance information unit of the facility device 1 shown in FIG. 2, an operation information database 14 serving as an operation information unit, and an external information database (not illustrated) serving as an external information unit. At least one of maintenance information, operation information, and external information is collected from at least one, processed, and output to the advanced analysis diagnosis unit 6 on the facility diagnosis center side C.

  That is, the contents of the secondary processing include, in addition to signal processing, a maintenance information database 13 storing information such as maintenance plans, equipment specifications, maintenance history, etc. in the user side B, process information, production plans, quality information, etc. It also includes a process of collecting external information such as an operation information database 14 storing information, a manufacturer's design specifications, product information, etc., and outputting it to the advanced analysis diagnosis unit 6 on the facility diagnosis center side C.

As an application example of the present invention, it can be applied to a method for managing and diagnosing the state of equipment .

A ... Factory, B ... User side, C ... Equipment diagnosis center side, 1 ... Equipment equipment, 1a ... Fan, 1b ... Pump, 2a, 2b ... Equipment status detector, 3 ... Equipment management data processing unit, 4 ... Equipment status Determination unit, 5 ... equipment monitoring unit, 6 ... advanced analysis diagnostic unit, 6a ... automatic diagnosis unit, 6b ... advanced data analysis unit, 6c ... trend management unit, 6d ... life prediction unit, 6d1 ... remaining life determination database, 6e ... Precision diagnosis part, 6f ... improvement method selection part, 6f1 ... improvement method database, 6g ... maintenance information part, 7a ... judgment field, 7b ... selection field, 7c ... channel number, 7d ... type of period, 7e ... graph display button, 8a ... primary diagnosis result column, 8b ... secondary diagnosis result column, 8c ... judgment column, 9 ... firewall, 10 ... communication network, 11 ... signal processing recipe, 11a ... SDP file, 11b ... wavelet file, 11c, 11c '... FFT Yl, 11d ... dimensionless signs parameter file, 11e ... multivariate analysis file, 11f ... other analysis file, 12 ... facility management data processing program group, 12a ... averaging processing program, 12b ... window function programs, 12c ... analysis program

Claims (1)

The equipment state detection information detected by the equipment state detection means attached to the equipment is signal-processed by the equipment management data processing unit, and the equipment state determination unit determines the level with respect to the management reference value,
When the level is determined from the facility state determination unit and determined to be abnormal, the related information of the facility device determined as abnormal is collected and processed by the facility monitoring unit , and the processing information processed through the communication network is the advanced analysis diagnosis unit To
Process information collected, processed and output by the equipment monitoring unit is subjected to advanced analysis by the advanced analysis / diagnostic unit to identify the cause of the abnormality of the corresponding equipment and its improvement measures, and the identified result is the equipment. a equipment diagnostic method for transmitting to the monitoring unit, when it is necessary to add to diagnose Upon the diagnosing advanced analysis and diagnosis unit based on the processing information pre SL outputted by treatment with facility monitoring unit upload from the advanced analysis diagnosis unit via a communications network from the facility management data processing programs provided in the high degree of analysis and diagnosis unit extracts a predetermined facility management data processing program to the facility monitoring section, An equipment diagnosis method characterized by repeating the above steps as many times as necessary .

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