CN111459700A - Method and apparatus for diagnosing device failure, diagnostic device, and storage medium - Google Patents
Method and apparatus for diagnosing device failure, diagnostic device, and storage medium Download PDFInfo
- Publication number
- CN111459700A CN111459700A CN202010264838.7A CN202010264838A CN111459700A CN 111459700 A CN111459700 A CN 111459700A CN 202010264838 A CN202010264838 A CN 202010264838A CN 111459700 A CN111459700 A CN 111459700A
- Authority
- CN
- China
- Prior art keywords
- fault
- parameter measuring
- sample
- equipment
- alternative
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
- G06F11/0751—Error or fault detection not based on redundancy
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Quality & Reliability (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses a method, a device, a diagnostic device and a storage medium for diagnosing equipment faults, wherein a first corresponding relation between a fault mode and a parameter measuring point under each equipment and an equipment fault database of a second corresponding relation between the fault mode and a fault sample under each equipment are established, no complex expert experience is required to be added, when equipment alarm parameter measuring point information is received, an alternative fault mode and an associated parameter measuring point corresponding to the alarm parameter measuring point of the equipment alarm parameter measuring point information are determined according to the first corresponding relation, an alternative fault sample is determined according to the second corresponding relation, and then a time sequence of the alarm parameter measuring point and the associated parameter measuring point in the same preset time period is obtained, the time sequence is compared with time sequence data of the alternative fault sample, namely, the association between the alarm parameter measuring point information and the fault sample is obtained through simple analysis of basic data, and determining a target fault sample according to the comparison result, and further determining a fault reason.
Description
Technical Field
The present invention relates to the field of device fault diagnosis technologies, and in particular, to a device fault diagnosis method, a device, and a storage medium.
Background
With the development of computers, the development of computer equipment monitoring systems is increasingly emphasized, and a development trend of intelligent monitoring and diagnosis systems is established. When there are a large number of devices that need to be monitored and diagnosed, or when critical devices need to be monitored continuously, it is very burdensome to perform data acquisition, analysis, and comparison frequently. At this time, the computer is used for automatic monitoring and diagnosis, so that a large amount of manpower and material resources can be saved, and the objectivity and accuracy of the diagnosis result can be ensured.
Computer monitoring and diagnostic systems can be divided into, according to the technology employed: simple automatic diagnosis; precise automatic diagnosis; an expert system for diagnosis.
The simple automatic diagnosis usually adopts some simple characteristic parameters, such as the root mean square value, peak value or kurtosis coefficient of the signal, to compare with the value of the standard reference state, so as to judge whether the fault exists, but not what kind of fault. The monitoring technology and equipment are simple, the operation is easy to master, and the price is low, so the method is widely applied.
The precise automatic diagnosis needs to comprehensively adopt various diagnosis technologies, further diagnose equipment which is considered to have abnormality in simple diagnosis (initial diagnosis) so as to determine the type and the part of the fault and predict the development of the fault, requires special technical personnel for operation, and generally needs personnel with abundant experience to participate in the aspects of giving diagnosis results, explaining, processing countermeasures and the like.
Unlike general precise automatic diagnosis, the diagnostic expert system is a computer diagnostic system based on artificial intelligence. It can simulate the thinking mode of fault diagnosis expert and use the existing fault diagnosis system. The method can simulate the thinking mode of a fault diagnosis expert, make reasoning judgment on the collected equipment information by using the existing fault diagnosis technical knowledge and expert experience, and continuously modify and supplement the knowledge to improve the performance of an expert system, so that the method is very effective for the diagnosis of a complex system and is a development direction of equipment fault diagnosis.
However, in order to implement fault diagnosis, the diagnostic expert system needs to establish an expert knowledge base in advance, and the expert knowledge base stores the knowledge and experience of the expert, and generally has two knowledge contents: firstly, aiming at a specific system, including the structure of the system, the fault phenomena of the system which often occur, the reason of each fault phenomenon and the logical relationship between the fault phenomena and the reasons; the second is expert experience for fault diagnosis of general equipment in the system. Because the expert knowledge base is highly dependent on the knowledge and experience of the expert when being created, the acquisition and expression of the expert knowledge become bottlenecks which restrict the development of the fault diagnosis expert system. Therefore, high cost is required for realizing automatic diagnosis of equipment faults of complex systems.
Disclosure of Invention
The invention aims to provide a method, a device, a diagnostic device and a storage medium for diagnosing equipment faults, which are used for reducing the dependence on expert knowledge and experience on the basis of realizing automatic diagnosis of the equipment faults of a complex system so as to reduce the cost of equipment fault diagnosis.
In order to solve the above technical problem, the present invention provides a method for diagnosing an equipment fault, including:
establishing a first corresponding relation between a fault mode and a parameter measuring point under each device and a second corresponding relation between the fault mode and the fault sample under each device according to the measuring point information, the device ledger and the fault sample to obtain a device fault database;
when equipment alarm parameter measuring point information is received, determining an alternative fault mode corresponding to an alarm parameter measuring point of the equipment alarm parameter measuring point information and associated parameter measuring points except the alarm parameter measuring point corresponding to the alternative fault mode in the equipment fault database according to the first corresponding relation;
according to the second corresponding relation, determining an alternative fault sample corresponding to an alternative fault mode in the equipment fault database;
acquiring time sequences of the alarm parameter measuring points and the associated parameter measuring points in the same preset time period;
comparing the time sequence with the time sequence data of the alternative fault samples, and selecting a target fault sample according to a comparison result;
and determining a fault reason according to the target fault sample.
Optionally, before determining, according to the second corresponding relationship, an alternative fault sample corresponding to an alternative fault mode in the equipment fault database, the method further includes:
performing statistical analysis on parameter measuring points corresponding to the fault mode and time sequence data of the parameter measuring points in a fault sample corresponding to the fault mode to obtain standard statistical characteristics of the parameter measuring points, and generating a standard characteristic vector of the fault mode according to the statistical characteristics;
performing statistical analysis on the time sequence to obtain fault statistical characteristics of the alarm parameter measuring points and the associated parameter measuring points, and generating fault characteristic vectors of the alternative fault modes according to the fault statistical characteristics;
calculating the similarity between the fault characteristic vector of each alternative fault mode and the standard characteristic vector of each alternative fault mode;
taking the preset number of the alternative fault modes with the highest similarity as the screened alternative fault modes;
correspondingly, the determining, according to the second correspondence, an alternative fault sample corresponding to an alternative fault mode in the equipment fault database specifically includes:
and determining the candidate fault sample corresponding to the screened candidate fault mode in the equipment fault database according to the second corresponding relation.
Optionally, the comparing the time sequence with the time sequence data of the candidate fault sample, and selecting a target fault sample according to a comparison result specifically includes:
calculating waveform similarity between the time series and the time series data;
calculating the similarity of statistical characteristics between the time sequence and the time sequence data according to the fault statistical characteristics corresponding to the time sequence and the sample statistical characteristics corresponding to the time sequence data;
and performing weighted operation on the waveform similarity and the statistical characteristic similarity, and determining the target fault sample according to a weighted operation result.
Optionally, the method further includes:
and determining the duration of the preset time period and the sampling frequency of the time sequence according to the alternative fault samples.
Optionally, after the target fault sample is selected according to the comparison result, the method further includes:
and storing the time sequence related to the target fault sample in the fault sample set in which the target fault sample is located.
Optionally, the method further includes:
and determining a fault solution according to the target fault sample.
Optionally, before comparing the time series with the time series data of the candidate fault samples, the method further includes:
and smoothing the curve of the time series.
In order to solve the above technical problem, the present invention further provides a device for diagnosing an equipment fault, including:
the device comprises an acquisition unit, a fault analysis unit and a fault analysis unit, wherein the acquisition unit is used for establishing a first corresponding relation between a fault mode and a parameter measuring point under each device and a second corresponding relation between the fault mode and the fault sample under each device according to measuring point information, a device ledger and a fault sample to obtain a device fault database;
a first query unit, configured to, when receiving device alarm parameter measurement point information, determine, according to the first correspondence, an alternative fault mode corresponding to an alarm parameter measurement point of the device alarm parameter measurement point information and associated parameter measurement points, other than the alarm parameter measurement point, corresponding to the alternative fault mode in the device fault database;
a second query unit, configured to determine, according to the second correspondence, an alternative fault sample corresponding to an alternative fault mode in the equipment fault database;
the acquisition unit is used for acquiring the time sequence of the alarm parameter measuring point and the associated parameter measuring point in the same preset time period;
the comparison unit is used for comparing the time sequence with the time sequence data of the alternative fault samples and selecting a target fault sample according to a comparison result;
and the output unit is used for determining a fault reason according to the target fault sample.
In order to solve the above technical problem, the present invention further provides a device for diagnosing a device fault, including:
a memory for storing instructions, the instructions comprising the steps of any one of the above-described method for diagnosing a fault in a device;
a processor to execute the instructions.
In order to solve the above technical problem, the present invention further provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for diagnosing a device fault as described in any one of the above.
The equipment fault diagnosis method provided by the invention establishes an equipment fault database based on the original data such as measuring point information, equipment ledgers, fault samples and the like, the method comprises a first corresponding relation between a fault mode and a parameter measuring point under each device and a second corresponding relation between the fault mode and a fault sample under each device, so that when the device alarm parameter measuring point information is received, namely, the alternative fault mode and the associated parameter measuring point corresponding to the alarm parameter measuring point of the equipment alarm parameter measuring point information can be determined according to the first corresponding relation, and determining an alternative fault sample according to the second corresponding relation, further acquiring a time sequence of the alarm parameter measuring point and the associated parameter measuring point in the same preset time period, comparing the time sequence with the time sequence data of the alternative fault sample, and finally determining a target fault sample according to a comparison result, namely determining the fault reason. Therefore, the method for diagnosing the equipment fault provided by the invention does not need to add complicated expert experience when the equipment fault database is established, and can obtain the fault reason only by simply analyzing basic data to obtain the association between the equipment alarm parameter measuring point information and the fault sample, thereby reducing the dependence on the expert knowledge and the experience on the basis of realizing the automatic diagnosis of the equipment fault of a complicated system and further reducing the cost for diagnosing the equipment fault.
The invention also provides a device for diagnosing the equipment fault, a diagnostic device and a storage medium, which have the beneficial effects and are not described again.
Drawings
In order to more clearly illustrate the embodiments or technical solutions of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
Fig. 1 is a flowchart of a method for diagnosing a device fault according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a fault logic tree according to an embodiment of the present invention;
FIG. 3 is a flow chart of another method for diagnosing equipment faults according to the embodiment of the invention;
fig. 4 is a schematic structural diagram of an apparatus for diagnosing a device fault according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a device for diagnosing a device fault according to an embodiment of the present invention.
Detailed Description
The core of the invention is to provide a method, a device and a storage medium for diagnosing equipment faults, which are used for reducing the dependence on expert knowledge and experience on the basis of realizing the automatic diagnosis of the equipment faults of a complex system, thereby reducing the cost of equipment fault diagnosis.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of a method for diagnosing a device fault according to an embodiment of the present invention; fig. 2 is a schematic structural diagram of a fault logic tree according to an embodiment of the present invention.
As shown in fig. 1, a method for diagnosing an equipment fault according to an embodiment of the present invention includes:
s101: and establishing a first corresponding relation between the fault mode and the parameter measuring point under each device and a second corresponding relation between the fault mode and the fault sample under each device according to the measuring point information, the device ledger and the fault sample to obtain a device fault database.
The state of the art diagnostic expert system is a rule-driven diagnostic scheme that relies on adding fault diagnosis rules, consisting of expert knowledge and experience, to an expert knowledge base. The method for diagnosing the equipment fault provided by the embodiment of the invention is a data-driven diagnosis scheme, and only needs to accumulate original data such as measuring point information, equipment ledgers and fault samples and form an equipment fault database by adopting a simple data analysis mode which is convenient for a computer to implement such as keyword search, screening and classification. The measuring point information comprises measuring point parameters and measuring point positions, wherein the measuring point parameters can be temperature, vibration, pressure, flow, current, voltage, positions and the like. The fault sample can be derived from actual cases, simulation data and expert experience, and specifically, data corresponding to one fault cause can be taken as one fault sample.
The first corresponding relation between the fault modes and the parameter measuring points under each device is that for the same device, different fault modes correspond to different parameter measuring points, for example, a certain fault mode corresponds to three parameter measuring points of temperature, vibration and pressure. And (3) a second corresponding relation between the fault modes and the fault samples of the equipment, namely that different fault modes correspond to different fault samples for the same equipment. A specific embodiment of building a fault database may be as follows.
The method for diagnosing the equipment fault provided by the embodiment of the invention can be used for diagnosing systems of different levels, such as the whole factory, key equipment, important parts of the key equipment and the like. Taking the device failure detection of the whole plant as an example, as shown in fig. 2, a failure logic tree of the plant may be established, non-leaf nodes under a plant or a project company may include a unit, a system, a device model, a component, and a failure mode, and each non-leaf node may be connected with a plurality of child nodes.
One piece of equipment may have different models (type a equipment, type B equipment … …), such as coal mill equipment which accomplishes the grinding of raw coal into coal fines, and the types of equipment which accomplishes this function are the three main types of steel ball mills, bowl mills and fan mills, which are all different in construction and in the location and physical significance of the parameter points.
Different equipment types have different parts (such as part 1 and part 2 … … in the type A equipment).
Each part has different fault modes (such as a fault mode 1 … … under the part 1; a fault mode 1 'under the part 2; a fault mode 2' … …), the fault modes are fault dominance performance characteristic sets, each fault mode stores typical characteristic values when the fault modes occur, for example, the typical characteristic values of the fault mode of large bearing bush vibration include parameter measuring points such as bearing bush vibration, X-direction shaft vibration, Y-direction shaft vibration and the like, and each parameter measuring point corresponds to different characteristic values. Different part units may have the same fault performance, but the types of the parameter measuring points corresponding to the fault modes and the characteristic values of the parameter measuring points cannot be completely consistent.
Further, the failure reason may be the leaf node of the failure logic tree, or a maintenance strategy may be further connected as the leaf node under the failure reason.
To build a fault logic tree as shown in fig. 2, it is necessary to accumulate raw data as described above and fill each node with a simple statistical analysis. The nodes such as the unit, the system, the equipment type and the parts can be directly generated through the machine account information of the monitored object. Starting from the node of the fault mode, the fault sample is required to be analyzed and extracted, the corresponding equipment type and parts of the fault sample are determined by adopting key word extraction and other modes in the fault sample, further, the display performance and the fault reason of the fault are extracted, a plurality of fault samples with the same or similar fault explicit performance corresponding to the same parts are classified into the same fault mode, the fault samples are classified according to different fault reasons, and optionally, the fault samples are further classified according to different maintenance strategies.
S102: when equipment alarm parameter measuring point information is received, according to the first corresponding relation, an alternative fault mode corresponding to an alarm parameter measuring point of the equipment alarm parameter measuring point information and associated parameter measuring points except the alarm parameter measuring point corresponding to the alternative fault mode are determined in an equipment fault database.
The equipment alarm parameter measuring point information is derived from an equipment Early Warning System (EWS), a Distributed Control System (DCS), a monitoring information system (SIS), a steam turbine monitoring system (TSI) and the like.
Since different types of equipment have completely different structures and different positions and physical meanings of parameter measuring points, the identification of the type of the equipment is an important ring for completing the diagnosis process. After receiving the equipment alarm parameter measuring point information, firstly, acquiring relevant information of a unit and equipment to which an alarm parameter measuring point corresponding to the equipment alarm parameter measuring point information belongs according to the equipment alarm parameter measuring point information and an equipment ledger, and further identifying the type of the equipment to which the parameter measuring point belongs.
Identifying the type of equipment that has failed is easy, but often does not directly identify the component that has failed. Therefore, according to the first corresponding relationship established in step S101, a fault mode including the parameter measurement point is found in each fault mode corresponding to the equipment type, and is determined as a candidate fault mode, and other parameter measurement points of each candidate fault mode are obtained, that is, associated parameter measurement points. If the alarm parameter measuring point corresponding to the equipment alarm parameter measuring point information is I, finding out a plurality of fault modes containing the I measuring point as alternative fault modes: { I, U, T }, { I, Q, T, P }, { I, W, T }, … …. And in the fault mode corresponding to the I, U, T, the parameter measuring point associated with the alarm parameter measuring point I is U, T, and so on.
S103: and determining an alternative fault sample corresponding to the alternative fault mode in the equipment fault database according to the second corresponding relation.
And obtaining a fault sample corresponding to each alternative fault mode according to the second corresponding relation established in the step S101, wherein the fault sample is an alternative fault sample.
S104: and acquiring time sequences of the alarm parameter measuring points and the associated parameter measuring points in the same preset time period.
Specifically, the time sequence of the warning parameter measuring point and the time sequence of the associated parameter measuring point in the same preset time period can be obtained from information sources such as an equipment Early Warning System (EWS), a Distributed Control System (DCS), a monitoring information system (SIS), a turbine monitoring system (TSI) and the like. The preset time period is a preset time period which is pushed forward when a fault of the equipment alarm parameter measuring point information occurs.
Different failure modes are set with different preset time periods to ensure the reliability of data because different failures have different development rates. If the development rate is high and the data change rate is high, a preset time period takes a small value; the fault mode with slow development rate and long duration takes a large value in the preset time period. Optionally, the method for diagnosing the device fault provided in the embodiment of the present invention further includes:
and determining the duration of a preset time period and the sampling frequency of the time sequence according to the alternative fault samples.
S105: and comparing the time sequence with the time sequence data of the alternative fault samples, and selecting the target fault sample according to the comparison result.
The time sequence of the obtained alarm parameter measuring point and the obtained associated parameter measuring point in the same preset time period is usually curve data, and in order to avoid interference of mutation data and error data, before comparing the time sequence with the time sequence data of the candidate fault sample, the method further comprises the following steps:
and smoothing the time series curve.
Specifically, the curve of the time series can be smoothed by a data processing method of removing outliers, reducing sampling frequency, and equalizing data.
And after the time series data of the candidate fault samples are processed in the same way, comparing the time series with the time series data of the candidate fault samples. For visual display, a comparison result of the time series and the time series data is expressed in a mode of calculating similarity. The type of the calculated similarity is specifically one of a euclidean distance, a manhattan distance, a cosine distance, a pearson correlation coefficient, and a spearman correlation coefficient.
According to the comparison result, the candidate fault sample with the highest similarity can be determined, namely the target fault sample.
S106: and determining a fault reason according to the target fault sample.
And according to the record of the target fault sample, determining the fault reason pointed by the equipment alarm parameter measuring point information.
In addition, if the device fault database established in step S101 includes a maintenance policy classification, the method for diagnosing a device fault according to the embodiment of the present invention further includes:
and determining a fault solution according to the target fault sample.
And outputting a fault reason and a fault solution, and completing the diagnosis of the equipment fault.
The equipment fault diagnosis method provided by the embodiment of the invention establishes the equipment fault database based on the original data such as the measuring point information, the equipment ledger, the fault sample and the like, the method comprises a first corresponding relation between a fault mode and a parameter measuring point under each device and a second corresponding relation between the fault mode and a fault sample under each device, so that when the device alarm parameter measuring point information is received, namely, the alternative fault mode and the associated parameter measuring point corresponding to the alarm parameter measuring point of the equipment alarm parameter measuring point information can be determined according to the first corresponding relation, and determining an alternative fault sample according to the second corresponding relation, further acquiring a time sequence of the alarm parameter measuring point and the associated parameter measuring point in the same preset time period, comparing the time sequence with the time sequence data of the alternative fault sample, and finally determining a target fault sample according to a comparison result, namely determining the fault reason. Therefore, the method for diagnosing the equipment fault provided by the embodiment of the invention does not need to add complicated expert experience when the equipment fault database is established, and can obtain the fault reason only by simply analyzing the basic data to obtain the association between the equipment alarm parameter measuring point information and the fault sample, thereby reducing the dependence on the expert knowledge and experience on the basis of realizing the automatic diagnosis of the equipment fault of a complicated system and further reducing the cost for diagnosing the equipment fault.
Fig. 3 is a flowchart of another method for diagnosing a device fault according to an embodiment of the present invention.
In the above embodiment, when the number of the candidate fault modes and the corresponding candidate fault samples is large, the calculation work of comparing the time sequence of the alarm parameter measuring point and the associated parameter measuring point in the same preset time period with the time sequence data of the candidate fault samples is extremely large, and the diagnosis rate is slow. In order to speed up the equipment fault diagnosis and reduce the calculation amount, on the basis of the above embodiment, the candidate fault samples are screened, and the number of the candidate fault samples needing to be compared is reduced. Specifically, in step S103, according to the second corresponding relationship, before determining the candidate fault sample corresponding to the candidate fault mode in the equipment fault database, the method for diagnosing the equipment fault according to the embodiment of the present invention further includes:
s301: and carrying out statistical analysis on the time sequence data of the parameter measuring points corresponding to the fault mode and the parameter measuring points in the fault sample corresponding to the fault mode to obtain standard statistical characteristics of the parameter measuring points, and generating a standard characteristic vector of the fault mode according to the statistical characteristics.
Each subordinate node of the fault mode, namely the fault reason, stores a fault sample set, the fault sample sets have common fault reasons, the fault dominance performances of the fault sample sets are similar, statistical analysis is carried out on fault dominance characteristics corresponding to the subordinate fault reasons, a fault dominance characteristic set of the fault mode is obtained, and typical characteristic values of the fault mode are stored in the set. For ease of calculation, the fault dominant signature set of the fault pattern is expressed in the form of a standard signature vector.
The specific way of generating the standard feature vector of the fault mode may specifically be to calculate statistical features of parameter measurement points of all fault samples belonging to the fault mode, such as a maximum value, a minimum value, a mean value, a median, a mean square error, a skewness, a kurtosis, and the like, and mark the statistical features as standard statistical features, so as to generate the standard feature vector of the fault mode by using each statistical feature. The standard feature vector is a high-dimensional vector, and each dimension corresponds to a statistical feature. Assuming that 10 parameter features are selected, 7 statistical features are calculated for each parameter feature, and a 70-dimensional standard feature vector is obtained.
S302: and carrying out statistical analysis on the time sequence to obtain the fault statistical characteristics of the alarm parameter measuring points and the associated parameter measuring points, and generating the fault characteristic vector of the alternative fault mode according to the fault statistical characteristics.
And according to the types of the parameter measuring points and the types of the statistical characteristics in the standard characteristic vector corresponding to the alternative fault mode, performing statistical analysis on the time sequences of the alarm parameter measuring points and the associated parameter measuring points acquired in the step S104 in the same preset time period to obtain the fault statistical characteristics of the alarm parameter measuring points and the associated parameter measuring points, and generating the fault characteristic vector of the alternative fault mode according to the fault statistical characteristics. If there are a plurality of candidate failure modes, a plurality of failure feature vectors are generated with reference to the standard feature vector of each candidate failure mode.
S303: and calculating the similarity between the fault characteristic vector of each candidate fault mode and the standard characteristic vector of each candidate fault mode.
The similarity between the fault feature vector of each candidate fault mode and the standard feature vector of each candidate fault mode can be calculated in a mode of calculating a Euclidean distance, a Manhattan distance, a cosine distance, a Pearson correlation coefficient, a Spireman correlation coefficient and the like.
S304: and taking the preset number of candidate fault modes with the highest similarity as the screened candidate fault modes.
And sequencing all the alternative fault modes from high to low according to the similarity, wherein the high probability of the alternative fault modes with the top similarity rank is the fault mode of the current alarm parameter measuring point, so that the alternative fault modes with the preset number are selected as the screened alternative fault modes.
Correspondingly, step S103 specifically includes:
s305: and determining the candidate fault sample corresponding to the screened candidate fault mode in the equipment fault database according to the second corresponding relation.
By screening the candidate failure modes, the number of failure samples that need to be compared is reduced.
On this basis, step S105: comparing the time sequence with the time sequence data of the alternative fault sample, and selecting a target fault sample according to the comparison result, which may specifically include:
calculating the waveform similarity between the time sequence and the time sequence data;
calculating the similarity of statistical characteristics between the time sequence and the time sequence data according to the fault statistical characteristics corresponding to the time sequence and the sample statistical characteristics corresponding to the time sequence data;
and performing weighted operation on the waveform similarity and the statistical characteristic similarity, and determining a target fault sample according to a weighted operation result.
The waveform similarity and the statistical feature similarity are properly matched according to the corresponding fault mode when the similarity calculation is carried out, the comprehensive similarity is calculated in a weighted mode, and the reliability of the equipment fault diagnosis result is improved.
On the basis of the foregoing embodiment, in the method for diagnosing a device fault according to the embodiment of the present invention, in order to continuously enrich the device fault database, after selecting a target fault sample according to a comparison result in step S105, the method further includes:
and storing the time sequence related to the target fault sample in the fault sample set in which the target fault sample is positioned.
In the above embodiment, if there are multiple candidate fault modes, multiple sets of corresponding time sequences are generated, and after a target fault sample is determined, the fault mode where the target fault sample is located is also determined, so as to obtain a set of time sequences of alarm parameter measuring points and associated parameter measuring points thereof, and store the time sequences in a fault sample set of the target fault sample in the fault mode.
Meanwhile, step S101 in the above embodiment may be repeated to continuously enrich and perfect the device failure database.
On the basis of the above detailed description of the various embodiments corresponding to the method for diagnosing the equipment fault, the invention also discloses a device for diagnosing the equipment fault, a diagnostic equipment and a storage medium corresponding to the method.
Fig. 4 is a schematic structural diagram of an apparatus fault diagnosis device according to an embodiment of the present invention.
As shown in fig. 4, the apparatus for diagnosing a device fault according to an embodiment of the present invention includes:
the acquisition unit 401 is configured to establish a first corresponding relationship between a fault mode and a parameter measurement point under each device and a second corresponding relationship between a fault mode and a fault sample under each device according to the measurement point information, the device ledger and the fault sample, so as to obtain a device fault database;
a first query unit 402, configured to, when receiving the device alarm parameter measurement point information, determine, according to the first corresponding relationship, an alternative fault mode corresponding to an alarm parameter measurement point of the device alarm parameter measurement point information and associated parameter measurement points, other than the alarm parameter measurement point, corresponding to the alternative fault mode, in the device fault database;
a second query unit 403, configured to determine, according to the second correspondence, an alternative fault sample corresponding to the alternative fault mode in the equipment fault database;
an obtaining unit 404, configured to obtain a time sequence of the alarm parameter measuring point and the associated parameter measuring point within the same preset time period;
a comparison unit 405, configured to compare the time sequence with time sequence data of the candidate fault sample, and select a target fault sample according to a comparison result;
and an output unit 406, configured to determine a failure cause according to the target failure sample.
Since the embodiments of the apparatus portion and the method portion correspond to each other, please refer to the description of the embodiments of the method portion for the embodiments of the apparatus portion, which is not repeated here.
Fig. 5 is a schematic structural diagram of a device for diagnosing a device fault according to an embodiment of the present invention.
As shown in fig. 5, the apparatus for diagnosing an apparatus fault according to an embodiment of the present invention includes:
a memory 510 for storing instructions, the instructions including the steps of the method for diagnosing a device fault according to any one of the above embodiments;
a processor 520 for executing the instructions.
The processor 520 may also include a main processor, which is a processor for Processing data in a wake-up state, also called a CPU (Central Processing Unit), and a coprocessor, which is a low power consumption processor for Processing data in a standby state, the processor 520 may be integrated with a GPU (Graphics Processing Unit) for rendering and rendering content to be displayed on a display screen, in some embodiments, the processor may also include an intelligent processor 520 for learning about AI (Artificial Intelligence processor) operations.
Memory 510 may include one or more storage media, which may be non-transitory. Memory 510 may also include high speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In this embodiment, the memory 510 is at least used for storing a computer program 511, wherein after the computer program 511 is loaded and executed by the processor 520, the relevant steps in the method for diagnosing the equipment failure disclosed in any of the foregoing embodiments can be implemented. In addition, the resources stored in the memory 510 may also include an operating system 512, data 513, and the like, and the storage manner may be a transient storage or a permanent storage. The operating system 512 may be Windows, among others. Data 513 may include, but is not limited to, data involved with the above-described methods.
In some embodiments, the device for diagnosing device failure may further include a display screen 530, a power supply 540, a communication interface 550, an input/output interface 560, a sensor 570, and a communication bus 580.
Those skilled in the art will appreciate that the configuration shown in fig. 5 does not constitute a limitation of the diagnostic device for device faults and may include more or fewer components than those shown.
The device fault diagnosis device provided by the embodiment of the application comprises the memory and the processor, and the processor can realize the device fault diagnosis method when executing the program stored in the memory, and the effect is the same as the effect.
It should be noted that the above-described embodiments of the apparatus and device are merely illustrative, for example, the division of modules is only one division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form. Modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present application may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and performs all or part of the steps of the methods according to the embodiments of the present invention, or all or part of the technical solution.
To this end, an embodiment of the present invention further provides a storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the diagnosis method such as the device failure.
The storage medium may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The computer program contained in the storage medium provided in the present embodiment is capable of implementing the steps of the method for diagnosing a device fault as described above when executed by the processor, and the same effect is obtained.
The present invention provides a method, an apparatus, a device and a storage medium for diagnosing a device failure. The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device, the equipment and the storage medium disclosed by the embodiment correspond to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A method of diagnosing equipment failure, comprising:
establishing a first corresponding relation between a fault mode and a parameter measuring point under each device and a second corresponding relation between the fault mode and the fault sample under each device according to the measuring point information, the device ledger and the fault sample to obtain a device fault database;
when equipment alarm parameter measuring point information is received, determining an alternative fault mode corresponding to an alarm parameter measuring point of the equipment alarm parameter measuring point information and associated parameter measuring points except the alarm parameter measuring point corresponding to the alternative fault mode in the equipment fault database according to the first corresponding relation;
according to the second corresponding relation, determining an alternative fault sample corresponding to an alternative fault mode in the equipment fault database;
acquiring time sequences of the alarm parameter measuring points and the associated parameter measuring points in the same preset time period;
comparing the time sequence with the time sequence data of the alternative fault samples, and selecting a target fault sample according to a comparison result;
and determining a fault reason according to the target fault sample.
2. The diagnostic method of claim 1, further comprising, prior to said determining, in said equipment failure database, an alternate failure sample corresponding to an alternate failure mode according to said second correspondence:
performing statistical analysis on parameter measuring points corresponding to the fault mode and time sequence data of the parameter measuring points in a fault sample corresponding to the fault mode to obtain standard statistical characteristics of the parameter measuring points, and generating a standard characteristic vector of the fault mode according to the statistical characteristics;
performing statistical analysis on the time sequence to obtain fault statistical characteristics of the alarm parameter measuring points and the associated parameter measuring points, and generating fault characteristic vectors of the alternative fault modes according to the fault statistical characteristics;
calculating the similarity between the fault characteristic vector of each alternative fault mode and the standard characteristic vector of each alternative fault mode;
taking the preset number of the alternative fault modes with the highest similarity as the screened alternative fault modes;
correspondingly, the determining, according to the second correspondence, an alternative fault sample corresponding to an alternative fault mode in the equipment fault database specifically includes:
and determining the candidate fault sample corresponding to the screened candidate fault mode in the equipment fault database according to the second corresponding relation.
3. The diagnostic method according to claim 2, wherein the comparing the time series with the time series data of the candidate fault samples and selecting a target fault sample according to the comparison result specifically comprises:
calculating waveform similarity between the time series and the time series data;
calculating the similarity of statistical characteristics between the time sequence and the time sequence data according to the fault statistical characteristics corresponding to the time sequence and the sample statistical characteristics corresponding to the time sequence data;
and performing weighted operation on the waveform similarity and the statistical characteristic similarity, and determining the target fault sample according to a weighted operation result.
4. The diagnostic method of claim 1, further comprising:
and determining the duration of the preset time period and the sampling frequency of the time sequence according to the alternative fault samples.
5. The diagnostic method of claim 1, further comprising, after the selecting a target fault sample based on the comparison,:
and storing the time sequence related to the target fault sample in the fault sample set in which the target fault sample is located.
6. The diagnostic method of claim 1, further comprising:
and determining a fault solution according to the target fault sample.
7. The diagnostic method of claim 1, further comprising, prior to said comparing said time series to said time series data of said candidate fault samples:
and smoothing the curve of the time series.
8. An apparatus for diagnosing a device failure, comprising:
the device comprises an acquisition unit, a fault analysis unit and a fault analysis unit, wherein the acquisition unit is used for establishing a first corresponding relation between a fault mode and a parameter measuring point under each device and a second corresponding relation between the fault mode and the fault sample under each device according to measuring point information, a device ledger and a fault sample to obtain a device fault database;
a first query unit, configured to, when receiving device alarm parameter measurement point information, determine, according to the first correspondence, an alternative fault mode corresponding to an alarm parameter measurement point of the device alarm parameter measurement point information and associated parameter measurement points, other than the alarm parameter measurement point, corresponding to the alternative fault mode in the device fault database;
a second query unit, configured to determine, according to the second correspondence, an alternative fault sample corresponding to an alternative fault mode in the equipment fault database;
the acquisition unit is used for acquiring the time sequence of the alarm parameter measuring point and the associated parameter measuring point in the same preset time period;
the comparison unit is used for comparing the time sequence with the time sequence data of the alternative fault samples and selecting a target fault sample according to a comparison result;
and the output unit is used for determining a fault reason according to the target fault sample.
9. A diagnostic apparatus for equipment failure, comprising:
a memory for storing instructions comprising the steps of the method of diagnosing a malfunction of the apparatus of any one of claims 1 to 7;
a processor to execute the instructions.
10. A storage medium on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method for diagnosing a malfunction of a device according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010264838.7A CN111459700B (en) | 2020-04-07 | 2020-04-07 | Equipment fault diagnosis method, diagnosis device, diagnosis equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010264838.7A CN111459700B (en) | 2020-04-07 | 2020-04-07 | Equipment fault diagnosis method, diagnosis device, diagnosis equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111459700A true CN111459700A (en) | 2020-07-28 |
| CN111459700B CN111459700B (en) | 2023-04-28 |
Family
ID=71685900
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010264838.7A Active CN111459700B (en) | 2020-04-07 | 2020-04-07 | Equipment fault diagnosis method, diagnosis device, diagnosis equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111459700B (en) |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112347641A (en) * | 2020-11-05 | 2021-02-09 | 华润电力技术研究院有限公司 | Water supply pump fault prediction and diagnosis method and system based on real-time data |
| CN112364491A (en) * | 2020-10-28 | 2021-02-12 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | System safety quantitative test method and device, computer equipment and storage medium |
| CN112379322A (en) * | 2020-11-05 | 2021-02-19 | 广东电网有限责任公司佛山供电局 | On-line monitoring method for capacitor voltage transformer |
| CN112580712A (en) * | 2020-12-15 | 2021-03-30 | 山东山大电力技术股份有限公司 | Fault processing aid decision method and system for relay protection device |
| CN113310693A (en) * | 2021-06-07 | 2021-08-27 | 华润电力技术研究院有限公司 | Mechanical fault detection method, device, equipment and storage medium |
| CN113447880A (en) * | 2021-05-28 | 2021-09-28 | 国网河北省电力有限公司营销服务中心 | Fault diagnosis method and device for electric energy meter and terminal equipment |
| CN113819575A (en) * | 2021-08-18 | 2021-12-21 | 青岛海尔空调器有限总公司 | Control method and device for air conditioner and server |
| CN114115044A (en) * | 2021-11-26 | 2022-03-01 | 武汉锐科光纤激光技术股份有限公司 | Monitoring method, system, device, storage medium and electronic device for welding equipment |
| CN114265878A (en) * | 2021-12-21 | 2022-04-01 | 深圳市汇拓新邦科技有限公司 | Method, device, computer equipment and storage medium for generating power alarm curve |
| CN114327964A (en) * | 2020-10-10 | 2022-04-12 | 腾讯科技（深圳）有限公司 | Method, device, equipment and storage medium for processing fault reasons of service system |
| CN114357262A (en) * | 2021-11-25 | 2022-04-15 | 中联重科股份有限公司 | Method, processor and server for engineering equipment |
| CN116132270A (en) * | 2022-12-27 | 2023-05-16 | 中国联合网络通信集团有限公司 | Fault detection method, device, equipment and readable storage medium |
| CN116188471A (en) * | 2023-05-04 | 2023-05-30 | 飞杨电源技术(深圳)有限公司 | Intelligent defect detection method for lithium iron phosphate battery charger |
| CN116577103A (en) * | 2022-01-26 | 2023-08-11 | 华晨宝马汽车有限公司 | Engine test abnormality analysis method, analysis system and computer storage medium |
| CN116594802A (en) * | 2023-07-17 | 2023-08-15 | 深圳开鸿数字产业发展有限公司 | Device failure prediction method, computer device and computer-readable storage medium |
| CN116678635A (en) * | 2023-05-25 | 2023-09-01 | 南京航空航天大学 | A performance testing system and method for a control-by-wire chassis based on digital twins |
| CN116821697A (en) * | 2023-08-30 | 2023-09-29 | 聊城莱柯智能机器人有限公司 | Mechanical equipment fault diagnosis method based on small sample learning |
| CN117472629A (en) * | 2023-11-02 | 2024-01-30 | 兰州航空职业技术学院 | A multi-fault diagnosis method and system for electronic information systems |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108241894A (en) * | 2018-01-10 | 2018-07-03 | 湖南大唐先科技有限公司 | Fault Locating Method, equipment and storage medium |
| CN108803569A (en) * | 2018-06-11 | 2018-11-13 | 哈尔滨锅炉厂有限责任公司 | Station boiler diagnostic expert system and its method for diagnosing faults |
| WO2019233047A1 (en) * | 2018-06-07 | 2019-12-12 | 国电南瑞科技股份有限公司 | Power grid dispatching-based operation and maintenance method |
-
2020
- 2020-04-07 CN CN202010264838.7A patent/CN111459700B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108241894A (en) * | 2018-01-10 | 2018-07-03 | 湖南大唐先科技有限公司 | Fault Locating Method, equipment and storage medium |
| WO2019233047A1 (en) * | 2018-06-07 | 2019-12-12 | 国电南瑞科技股份有限公司 | Power grid dispatching-based operation and maintenance method |
| CN108803569A (en) * | 2018-06-11 | 2018-11-13 | 哈尔滨锅炉厂有限责任公司 | Station boiler diagnostic expert system and its method for diagnosing faults |
Non-Patent Citations (1)
| Title |
|---|
| 胡瑾秋等: "非正常工况下化工过程设备故障实时关联预警研究", 《中国安全科学学报》 * |
Cited By (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114327964A (en) * | 2020-10-10 | 2022-04-12 | 腾讯科技（深圳）有限公司 | Method, device, equipment and storage medium for processing fault reasons of service system |
| CN112364491A (en) * | 2020-10-28 | 2021-02-12 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | System safety quantitative test method and device, computer equipment and storage medium |
| CN112347641A (en) * | 2020-11-05 | 2021-02-09 | 华润电力技术研究院有限公司 | Water supply pump fault prediction and diagnosis method and system based on real-time data |
| CN112379322A (en) * | 2020-11-05 | 2021-02-19 | 广东电网有限责任公司佛山供电局 | On-line monitoring method for capacitor voltage transformer |
| CN112347641B (en) * | 2020-11-05 | 2024-06-11 | 深圳市出新知识产权管理有限公司 | Method and system for predicting and diagnosing faults of water supply pump based on real-time data |
| CN112580712A (en) * | 2020-12-15 | 2021-03-30 | 山东山大电力技术股份有限公司 | Fault processing aid decision method and system for relay protection device |
| CN113447880A (en) * | 2021-05-28 | 2021-09-28 | 国网河北省电力有限公司营销服务中心 | Fault diagnosis method and device for electric energy meter and terminal equipment |
| CN113447880B (en) * | 2021-05-28 | 2022-07-22 | 国网河北省电力有限公司营销服务中心 | Fault diagnosis method and device for electric energy meter and terminal equipment |
| CN113310693A (en) * | 2021-06-07 | 2021-08-27 | 华润电力技术研究院有限公司 | Mechanical fault detection method, device, equipment and storage medium |
| CN113819575A (en) * | 2021-08-18 | 2021-12-21 | 青岛海尔空调器有限总公司 | Control method and device for air conditioner and server |
| CN114357262A (en) * | 2021-11-25 | 2022-04-15 | 中联重科股份有限公司 | Method, processor and server for engineering equipment |
| CN114115044B (en) * | 2021-11-26 | 2023-09-01 | 武汉锐科光纤激光技术股份有限公司 | Monitoring method, system and device of welding equipment, storage medium and electronic device |
| CN114115044A (en) * | 2021-11-26 | 2022-03-01 | 武汉锐科光纤激光技术股份有限公司 | Monitoring method, system, device, storage medium and electronic device for welding equipment |
| CN114265878A (en) * | 2021-12-21 | 2022-04-01 | 深圳市汇拓新邦科技有限公司 | Method, device, computer equipment and storage medium for generating power alarm curve |
| CN116577103A (en) * | 2022-01-26 | 2023-08-11 | 华晨宝马汽车有限公司 | Engine test abnormality analysis method, analysis system and computer storage medium |
| CN116132270A (en) * | 2022-12-27 | 2023-05-16 | 中国联合网络通信集团有限公司 | Fault detection method, device, equipment and readable storage medium |
| CN116188471A (en) * | 2023-05-04 | 2023-05-30 | 飞杨电源技术(深圳)有限公司 | Intelligent defect detection method for lithium iron phosphate battery charger |
| CN116678635A (en) * | 2023-05-25 | 2023-09-01 | 南京航空航天大学 | A performance testing system and method for a control-by-wire chassis based on digital twins |
| CN116594802A (en) * | 2023-07-17 | 2023-08-15 | 深圳开鸿数字产业发展有限公司 | Device failure prediction method, computer device and computer-readable storage medium |
| CN116821697A (en) * | 2023-08-30 | 2023-09-29 | 聊城莱柯智能机器人有限公司 | Mechanical equipment fault diagnosis method based on small sample learning |
| CN116821697B (en) * | 2023-08-30 | 2024-05-28 | 聊城莱柯智能机器人有限公司 | A mechanical equipment fault diagnosis method based on small sample learning |
| CN117472629A (en) * | 2023-11-02 | 2024-01-30 | 兰州航空职业技术学院 | A multi-fault diagnosis method and system for electronic information systems |
| CN117472629B (en) * | 2023-11-02 | 2024-05-28 | 兰州航空职业技术学院 | Multi-fault diagnosis method and system for electronic information system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111459700B (en) | 2023-04-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111459700B (en) | Equipment fault diagnosis method, diagnosis device, diagnosis equipment and storage medium | |
| CN116450399B (en) | Fault diagnosis and root cause positioning method for micro service system | |
| EP3795975B1 (en) | Abnormality sensing apparatus, abnormality sensing method, and abnormality sensing program | |
| US11016477B2 (en) | Devices, methods, and systems for a distributed rule based automated fault detection | |
| US20160255109A1 (en) | Detection method and apparatus | |
| CN105677791A (en) | Method and system used for analyzing operating data of wind generating set | |
| CN116150666B (en) | Energy storage system fault detection method and device and intelligent terminal | |
| CN111949429A (en) | Server fault monitoring method and system based on density clustering algorithm | |
| CN117647725B (en) | Aging test method and system for PCBA | |
| CN115659271A (en) | Sensor anomaly detection method, model training method, system, device and medium | |
| CN119026056A (en) | Energy storage system health monitoring method based on big data analysis | |
| CN117609911A (en) | Abnormal identification method and device for sensing equipment | |
| CN114037010A (en) | Method and device for identifying abnormal electric quantity data | |
| CN116302984B (en) | A root cause analysis method, device and related equipment for test tasks | |
| US20210080924A1 (en) | Diagnosis Method and Diagnosis System for a Processing Engineering Plant and Training Method | |
| CN117591994A (en) | Power equipment status data prediction methods, intelligent platforms, equipment and media | |
| CN117407245A (en) | Model training task anomaly detection method and system, electronic equipment and storage medium | |
| WO2021176576A1 (en) | Unsteadiness detection system, unsteadiness detection method, and unsteadiness detection program | |
| JP7062505B2 (en) | Equipment management support system | |
| KR102706766B1 (en) | Apparatus and Method for Generating Evaluation Data based on Operating Data of Control System | |
| WO2022059183A1 (en) | Information processing device, information processing method, and information processing program | |
| JP7771576B2 (en) | Device management system, method for estimating the cause of device failure, and program | |
| CN109474445B (en) | Distributed system root fault positioning method and device | |
| CN118519818B (en) | Deep recursion network-based big data computer system fault detection method | |
| CN117194049B (en) | Cloud host intelligent behavior analysis method and system based on machine learning algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20240408 Address after: 518066 Room 201, building A, No. 1, Qian Wan Road, Qianhai Shenzhen Hong Kong cooperation zone, Shenzhen, Guangdong (Shenzhen Qianhai business secretary Co., Ltd.) Patentee after: Shenzhen goes out new knowledge property right management Co.,Ltd. Country or region after: China Address before: 523808 Room 308, unit 1, building 18, no.6, Libin Road, Songshanhu Park, Dongguan City, Guangdong Province Patentee before: CR POWER TECHNOLOGY INSTITUTE Co.,Ltd. Country or region before: China |
|
| TR01 | Transfer of patent right |