CN113569950B - Power station equipment fault monitoring model generation method, system and device - Google Patents
Power station equipment fault monitoring model generation method, system and device Download PDFInfo
- Publication number
- CN113569950B CN113569950B CN202110857346.3A CN202110857346A CN113569950B CN 113569950 B CN113569950 B CN 113569950B CN 202110857346 A CN202110857346 A CN 202110857346A CN 113569950 B CN113569950 B CN 113569950B
- Authority
- CN
- China
- Prior art keywords
- training
- cluster
- data
- algorithm
- training data
- 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.)
- Active
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 93
- 238000000034 method Methods 0.000 title claims abstract description 71
- 238000012549 training Methods 0.000 claims abstract description 108
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 42
- 238000012545 processing Methods 0.000 claims abstract description 22
- 230000008569 process Effects 0.000 claims abstract description 17
- 239000011159 matrix material Substances 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 11
- 238000012360 testing method Methods 0.000 claims description 11
- 238000005192 partition Methods 0.000 claims description 4
- 238000012546 transfer Methods 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 9
- 230000006872 improvement Effects 0.000 description 8
- 238000004519 manufacturing process Methods 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000001514 detection method Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000005055 memory storage Effects 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 229920001296 polysiloxane Polymers 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 239000010979 ruby Substances 0.000 description 1
- 229910001750 ruby Inorganic materials 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Business, Economics & Management (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Economics (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Geometry (AREA)
- Computer Hardware Design (AREA)
- Software Systems (AREA)
- Probability & Statistics with Applications (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a power station equipment fault monitoring model generation method, a system and a device, wherein the method comprises the following steps: presetting the maximum capacity of an initial training sample set, and obtaining the training sample set containing training data by carrying out standardized processing on a large amount of historical normal data stored in a database based on the maximum capacity; determining the optimal cluster number by calculating and comparing Calinski-Harabasz score values under training data under different clusters, taking the determined optimal cluster number as the cluster number of a K-means algorithm, and determining a cluster center set of the data by training the training data through the algorithm; and taking the determined cluster center set as an initial cluster center of the FCM algorithm, training data through the algorithm to determine cluster membership of the data, establishing an FCM model, classifying the training data according to the maximum membership, and establishing corresponding PCA models according to different categories to complete the training process.
Description
Technical Field
The invention relates to the technical field of power station monitoring, in particular to a power station equipment fault monitoring model generation method, a system and a device.
Background
In the prior art, fault Detection (Fault Detection) is used to discover and confirm a system in time when the system fails, and give a corresponding display or alarm. Early detection of faults can provide important warnings of upcoming problems, and appropriate measures are taken to avoid serious accidents. PCA (Principle Component Analysis) fault monitoring method belongs to the fault monitoring method in the field of statistical learning. The method has the characteristics of high efficiency, convenient calculation and no dependence on fault variables, and is widely used in recent years.
The fault monitoring of the power station equipment is an important link of safe and economic operation of the power station, and the accuracy of the monitoring method is continuously improved, so that the personal and property safety of the power station can be effectively ensured. Meanwhile, because the power station is a typical multimode system, parameters under different working conditions show different statistical characteristics, and a single PCA monitoring model is difficult to learn the statistical characteristics, a better monitoring effect cannot be obtained.
Disclosure of Invention
The invention aims to provide a power station equipment fault monitoring model generation method, system and device, and aims to solve the problems in the prior art.
The invention provides a power station equipment fault monitoring model generation method, which comprises the following steps:
presetting the maximum capacity of an initial training sample set, and obtaining the training sample set containing training data by carrying out standardized processing on a large amount of historical normal data stored in a database based on the maximum capacity;
Determining the optimal cluster number by calculating and comparing Calinski-Harabasz score values under training data under different clusters, taking the determined optimal cluster number as the cluster number of a K-means algorithm, and determining a cluster center set of the data by training the training data through the algorithm;
And taking the determined cluster center set as an initial cluster center of the FCM algorithm, training data through the algorithm to determine cluster membership of the data, establishing an FCM model, classifying the training data according to the maximum membership, and establishing corresponding PCA models according to different categories to complete the training process.
The invention provides a power station equipment fault monitoring model generation system, which comprises:
The standardized module is used for presetting the maximum capacity of the initial training sample set, and obtaining the training sample set containing training data by carrying out standardized processing on a large amount of historical normal data stored in the database based on the maximum capacity;
The clustering module is used for determining the optimal cluster number by calculating and comparing Calinski-Harabasz score values under training data under different cluster groups, taking the determined optimal cluster number as the cluster number of the K-means algorithm, and determining a clustering center set of the data by training the training data through the algorithm;
the training module is used for taking the determined cluster center set as an initial cluster center of the FCM algorithm, training the training data through the algorithm to determine cluster membership of the data, establishing an FCM model, classifying the training data according to the maximum membership, and establishing corresponding PCA models according to different categories to complete the training process.
The embodiment of the invention also provides a power station equipment fault monitoring model generating device, which comprises the following steps: the power station equipment fault monitoring model generating method comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the computer program is executed by the processor to realize the steps of the power station equipment fault monitoring model generating method.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores an information transmission implementation program, and the program is executed by a processor to realize the steps of the power station equipment fault monitoring model generation method.
By adopting the embodiment of the invention, the training time can be obviously reduced by fully utilizing the historical data training result through the monitoring model, and the on-line monitoring process of the industrial process can be very facilitated. In addition, modeling complexity is reduced by using a clustering method, the problem of fault monitoring in multiple modes is effectively solved, and calculation complexity meets the requirement of on-line monitoring. The technical scheme of the embodiment of the invention is not supported by manual priori knowledge, is completely driven by data, meets the requirements of various multi-mode fault monitoring, and is beneficial to solving the fault monitoring problem of a complex model.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a power plant equipment fault monitoring model generation method of an embodiment of the present invention;
FIG. 2 is a detailed flow chart of a power plant equipment fault monitoring model generation method of an embodiment of the present invention;
FIG. 3 is a schematic diagram of a power plant equipment fault monitoring model generation system according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a power station equipment fault monitoring model generating device according to an embodiment of the present invention.
Detailed Description
In view of the problems existing in the prior art, the clustering algorithm can effectively reduce the complexity of the model, simplify the statistical characteristics of the multi-mode system, and is an effective method for solving the multi-mode fault monitoring. At present, the number of clusters has a remarkable influence on the clustering effect. Therefore, searching for the optimal cluster number using Calinski-Harabasz score values is a very efficient method. The clustering algorithm based on KFCM has reasoning logic similar to manual classification, and is more suitable for the clustering classification of multi-mode problems. Therefore, the embodiment of the invention provides a fault monitoring method, a fault monitoring system and a fault monitoring device based on a KFCM and PCA model in the field of machine learning. The method comprises the steps of firstly carrying out standardized processing on normal history working condition data stored in a database to obtain training samples, then carrying out classification processing on the training samples by using a KFCC algorithm, establishing an FCM model, and respectively establishing a PCA monitoring model according to different categories. After the test sample is subjected to standardization processing, determining model membership by the FCM model, and weighting monitoring statistics by each model to obtain a monitoring result. The method of the invention makes full use of the historical data training result of the PCA monitoring model, can obviously improve the monitoring accuracy of the PCA under the multi-working condition state, and is very beneficial to the industrial process monitoring, in particular to the process monitoring of power station equipment.
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise. Furthermore, the terms "mounted," "connected," "coupled," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Method embodiment
According to an embodiment of the present invention, there is provided a power station equipment fault monitoring model generating method, and fig. 1 is a flowchart of the power station equipment fault monitoring model generating method according to the embodiment of the present invention, as shown in fig. 1, where the power station equipment fault monitoring model generating method according to the embodiment of the present invention specifically includes:
Step 101, presetting the maximum capacity of an initial training sample set, and obtaining the training sample set containing training data by carrying out standardized processing on a large amount of historical normal data stored in a database based on the maximum capacity;
102, determining the optimal cluster number by calculating and comparing Calinski-Harabasz score values under training data under different clusters, taking the determined optimal cluster number as the cluster number of a K-means algorithm, and determining a cluster center set of the data by training the training data through the algorithm; step 102 specifically includes:
Determining the optimal cluster number by calculating and comparing Calinski-Harabasz score values under training data under different clusters specifically comprises:
According to formula 1, determining the optimal cluster number by calculating and comparing Calinski-Harabasz score values under training data under different clusters:
Where s (k) represents the ratio between the inter-class variance and the intra-class variance, B k represents the inter-class variance, W k represents the intra-class variance, tr (x) represents the trace of the matrix, i.e. the sum of diagonal elements of the matrix, m is the number of clusters, and k is the current class.
Step 103, taking the determined cluster center set as an initial cluster center of the FCM algorithm, training data to determine cluster membership of the data through the algorithm, establishing an FCM model, classifying the training data according to the maximum membership, and establishing corresponding PCA models according to different categories to complete the training process. Step 103 specifically includes:
training the training data through an algorithm to determine cluster membership of the data and establishing an FCM model specifically comprises the following steps:
training the training data via an algorithm to determine cluster membership of the data and establishing an FCM model according to the formula 2 and the formula 3:
Wherein u ij is the membership degree of the ith sample to the jth type, x i is the ith sample, C j is the jth cluster center, C k is the kth cluster center, k=1, 2, …, C, m is a weighted index for controlling the ambiguity of the partition, C is the number of cluster centers, C j is the updated cluster center, N is the number of samples, And the updated membership matrix.
In an embodiment of the present invention, the method may further include:
and obtaining a test sample set from the field data through standardized processing, determining the membership degree of the test sample set through the FCM model, obtaining monitoring statistics through each PCA monitoring model, and obtaining a final monitoring result according to the monitoring statistics and membership degree weighting.
The above technical solutions of the embodiments of the present invention are described in detail below with reference to the accompanying drawings.
The embodiment of the invention provides a fault monitoring method based on a KFCM and PCA model for solving the problem of multi-mode fault monitoring, which comprises the following steps:
Step 1, a large amount of historical normal data stored in a database is subjected to standardized processing to obtain a training sample set D= { x 1,x2,...,xl }, and the maximum capacity M of an initial training sample set is preset;
Step 2, determining the optimal cluster number by calculating and comparing Calinski-Harabasz score values under training data under different clusters, wherein the calculation formula is shown as a formula (A);
Step 3, taking the optimal cluster number determined in the step 2 as the cluster number of the K-means algorithm, and training data through the algorithm to determine a cluster center set of the data;
step 4, taking the cluster center set determined in the step 3 as an initial cluster center of the FCM algorithm, training data through the algorithm to determine cluster membership of the data, and establishing an FCM model, wherein specific iterative modeling processes are shown as formulas (B) and (C);
Step 5, classifying the training data according to the maximum membership degree, and establishing a corresponding PCA model according to different categories, so that the training process is completed;
Step 6, obtaining a test sample set T= { x 1,x2,...,xl }, by means of standardization processing, of the field data;
and 7, determining the membership degree of the test sample set through the FCM model, acquiring monitoring statistics by each PCA monitoring model, and acquiring a final monitoring result according to the monitoring statistics and membership degree weighting.
The specific implementation steps of the multi-mode modeling method provided by the invention are described below by taking power station equipment fault monitoring as an example, and the whole fault monitoring process mainly comprises model off-line training and model on-line monitoring. The detailed flow is shown in fig. 2:
Step1, field data from DCS enters a data input interface machine through a network switch;
step 2, respectively acquiring training samples and monitoring samples under different working conditions after the historical data and the real-time operation data of the power station are respectively subjected to a data preprocessing link;
Step 3, after determining the number of clusters by using a cluster evaluation method, initializing cluster centers by using a training sample through a K-Means algorithm, and then iteratively training an FCM model by taking the number of clusters and the cluster centers as initial parameters;
Step 4, training a sub PCA monitoring model after classifying the training samples through the FCM model, and completing a training process;
Step 5, obtaining a sample membership degree of a monitoring sample through an FCM model, and obtaining a monitoring result through weighting of each sub PCA modeling model;
And 6, monitoring the fault state of the power station equipment on line in real time by means of the obtained fault monitoring model, and outputting monitoring data and guidance comments to the client through the fault monitoring system.
In summary, by adopting the embodiment of the invention, the training time can be obviously reduced by fully utilizing the historical data training result through the monitoring model, which is very beneficial to the industrial process on-line monitoring process. In addition, modeling complexity is reduced by using a clustering method, the problem of fault monitoring in multiple modes is effectively solved, and calculation complexity meets the requirement of on-line monitoring. The technical scheme of the embodiment of the invention is not supported by manual priori knowledge, is completely driven by data, meets the requirements of various multi-mode fault monitoring, and is beneficial to solving the fault monitoring problem of a complex model.
System embodiment
According to an embodiment of the present invention, there is provided a power station equipment fault monitoring model generating system, and fig. 3 is a schematic diagram of the power station equipment fault monitoring model generating system according to the embodiment of the present invention, as shown in fig. 3, where the power station equipment fault monitoring model generating system according to the embodiment of the present invention specifically includes:
The normalization module 30 is configured to preset a maximum capacity of the initial training sample set, and obtain a training sample set containing training data by performing normalization processing on a large amount of historical normal data stored in the database based on the maximum capacity;
The clustering module 32 is configured to determine an optimal cluster number by calculating and comparing Calinski-Harabasz score values under training data under different clusters, take the determined optimal cluster number as a cluster number of a K-means algorithm, and determine a cluster center set of the training data through algorithm training; the clustering module 32 is specifically configured to:
According to formula 1, determining the optimal cluster number by calculating and comparing Calinski-Harabasz score values under training data under different clusters:
Where s (k) represents the ratio between the inter-class variance and the intra-class variance, B k represents the inter-class variance, W k represents the intra-class variance, tr (x) represents the trace of the matrix, i.e. the sum of diagonal elements of the matrix, m is the number of clusters, and k is the current class.
The training module 34 is configured to use the determined cluster center set as an initial cluster center of the FCM algorithm, train the training data to determine cluster membership of the data via the algorithm, establish an FCM model, classify the training data according to the maximum membership, and establish a corresponding PCA model according to different classes, thereby completing the training process. The training module 34 is specifically configured to:
training the training data via an algorithm to determine cluster membership of the data and establishing an FCM model according to the formula 2 and the formula 3:
Wherein u ij is the membership degree of the ith sample to the jth type, x i is the ith sample, C j is the jth cluster center, C k is the kth cluster center, k=1, 2, …, C, m is a weighted index for controlling the ambiguity of the partition, C is the number of cluster centers, C j is the updated cluster center, N is the number of samples, And the updated membership matrix.
In an embodiment of the present invention, the system further includes:
The monitoring module is used for obtaining the field data through standardized processing to obtain a test sample set, determining the membership degree of the test sample set through the FCM model, obtaining monitoring statistics through each PCA monitoring model, and obtaining a final monitoring result according to the monitoring statistics and membership degree weighting.
The embodiment of the present invention is a system embodiment corresponding to the above method embodiment, and specific operations of each module may be understood by referring to the description of the method embodiment, which is not repeated herein.
Device embodiment 1
An embodiment of the present invention provides a power station equipment fault monitoring model generating device, as shown in fig. 4, including: memory 40, processor 42, and a computer program stored on the memory 40 and executable on the processor 42, which when executed by the processor 42, performs the steps as described in the method embodiments.
Device example two
Embodiments of the present invention provide a computer-readable storage medium having stored thereon a program for carrying out information transmission, which when executed by the processor 42, carries out the steps as described in the method embodiments.
The computer readable storage medium of the present embodiment includes, but is not limited to: ROM, RAM, magnetic or optical disks, etc.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In the 30 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable GATE ARRAY, FPGA)) is an integrated circuit whose logic functions are determined by user programming of the device. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented with "logic compiler (logic compiler)" software, which is similar to the software compiler used in program development and writing, and the original code before being compiled is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but HDL is not just one, but a plurality of kinds, such as ABEL(Advanced Boolean Expression Language)、AHDL(Altera Hardware Description Language)、Confluence、CUPL(Cornell University Programming Language)、HDCal、JHDL(Java Hardware Description Language)、Lava、Lola、MyHDL、PALASM、RHDL(Ruby Hardware Description Language), and VHDL (Very-High-SPEED INTEGRATED Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application SPECIFIC INTEGRATED Circuits (ASICs), programmable logic controllers, and embedded microcontrollers, examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each unit may be implemented in the same piece or pieces of software and/or hardware when implementing the embodiments of the present specification.
One skilled in the relevant art will recognize that one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present description is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that 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 one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
One or more embodiments of the present specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing description is by way of example only and is not intended to limit the present disclosure. Various modifications and changes may occur to those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. that fall within the spirit and principles of the present document are intended to be included within the scope of the claims of the present document.
Claims (8)
1. The utility model provides a power station equipment fault monitoring model generation method which is characterized by comprising the following steps:
presetting the maximum capacity of an initial training sample set, and obtaining the training sample set containing training data by carrying out standardized processing on a large amount of historical normal data stored in a database based on the maximum capacity;
Determining the optimal cluster number by calculating and comparing Calinski-Harabasz score values under training data under different clusters, taking the determined optimal cluster number as the cluster number of a K-means algorithm, and determining a cluster center set of the data by training the training data through the algorithm;
Taking the determined cluster center set as an initial cluster center of an FCM algorithm, training data to determine cluster membership of the data through the algorithm, establishing an FCM model, classifying the training data according to the maximum membership, and establishing corresponding PCA monitoring models according to different categories to complete the training process;
and obtaining a test sample set from the field data through standardized processing, determining the membership degree of the test sample set through the FCM model, obtaining monitoring statistics through each PCA monitoring model, and obtaining a final monitoring result according to the monitoring statistics and membership degree weighting.
2. The method of claim 1, wherein determining the optimal cluster number by calculating Calinski-Harabasz score values for training data under different clusters comprises:
According to formula 1, determining the optimal cluster number by calculating and comparing Calinski-Harabasz score values under training data under different clusters:
where s (k) represents the ratio between the inter-class variance and the intra-class variance, B k represents the inter-class variance, W k represents the intra-class variance, tr (x) represents the trace of the matrix, i.e. the sum of the diagonal elements of the matrix, m is the number of clusters, and k is the current class.
3. The method of claim 1, wherein training the training data to determine cluster membership of the data via an algorithm and building the FCM model specifically comprises:
training the training data via an algorithm to determine cluster membership of the data and establishing an FCM model according to the formula 2 and the formula 3:
Wherein u ij is the membership degree of the ith sample to the jth type, x i is the ith sample, C j is the jth cluster center, C k is the kth cluster center, k=1, 2, …, C, m is a weighted index for controlling the ambiguity of the partition, C is the number of cluster centers, C j is the updated cluster center, N is the number of samples, and u ij m is the updated membership degree matrix.
4. A power plant equipment fault monitoring model generation system, comprising:
The standardized module is used for presetting the maximum capacity of the initial training sample set, and obtaining the training sample set containing training data by carrying out standardized processing on a large amount of historical normal data stored in the database based on the maximum capacity;
The clustering module is used for determining the optimal cluster number by calculating and comparing Calinski-Harabasz score values under training data under different cluster groups, taking the determined optimal cluster number as the cluster number of the K-means algorithm, and determining a clustering center set of the data by training the training data through the algorithm;
the training module is used for taking the determined cluster center set as an initial cluster center of the FCM algorithm, training data to determine cluster membership of the data through the algorithm, establishing an FCM model, classifying the training data according to the maximum membership, and establishing corresponding PCA monitoring models according to different categories to complete the training process;
The monitoring module is used for obtaining the field data through standardized processing to obtain a test sample set, determining the membership degree of the test sample set through the FCM model, obtaining monitoring statistics through each PCA monitoring model, and obtaining a final monitoring result according to the monitoring statistics and membership degree weighting.
5. The system of claim 4, wherein the clustering module is specifically configured to:
According to formula 1, determining the optimal cluster number by calculating and comparing Calinski-Harabasz score values under training data under different clusters:
where s (k) represents the ratio between the inter-class variance and the intra-class variance, B k represents the inter-class variance, W k represents the intra-class variance, tr (x) represents the trace of the matrix, i.e. the sum of the diagonal elements of the matrix, m is the number of clusters, and k is the current class.
6. The system of claim 4, wherein the training module is specifically configured to:
training the training data via an algorithm to determine cluster membership of the data and establishing an FCM model according to the formula 2 and the formula 3:
Wherein u ij is the membership degree of the ith sample to the jth type, x i is the ith sample, C j is the jth cluster center, C k is the kth cluster center, k=1, 2, …, C, m is a weighted index for controlling the ambiguity of the partition, C is the number of cluster centers, C j is the updated cluster center, N is the number of samples, And the updated membership matrix.
7. A power plant equipment fault monitoring model generation device, characterized by comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the power plant fault monitoring model generation method of any one of claims 1 to 3.
8. A computer-readable storage medium, wherein a program for realizing information transfer is stored on the computer-readable storage medium, and the program when executed by a processor realizes the steps of the power station equipment fault monitoring model generation method according to any one of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110857346.3A CN113569950B (en) | 2021-07-28 | 2021-07-28 | Power station equipment fault monitoring model generation method, system and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110857346.3A CN113569950B (en) | 2021-07-28 | 2021-07-28 | Power station equipment fault monitoring model generation method, system and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113569950A CN113569950A (en) | 2021-10-29 |
| CN113569950B true CN113569950B (en) | 2024-05-28 |
Family
ID=78168466
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110857346.3A Active CN113569950B (en) | 2021-07-28 | 2021-07-28 | Power station equipment fault monitoring model generation method, system and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113569950B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114417703B (en) * | 2021-12-22 | 2023-01-17 | 中国大唐集团科学技术研究院有限公司西北电力试验研究院 | Photovoltaic module fault online diagnosis and analysis algorithm |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103150731A (en) * | 2013-03-07 | 2013-06-12 | 南京航空航天大学 | Fuzzy clustering image segmenting method |
| CN103440505A (en) * | 2013-09-16 | 2013-12-11 | 重庆邮电大学 | Spatial neighborhood information weighted hyper-spectral remote sensing image classification method |
| US9336302B1 (en) * | 2012-07-20 | 2016-05-10 | Zuci Realty Llc | Insight and algorithmic clustering for automated synthesis |
| CN105929812A (en) * | 2016-04-18 | 2016-09-07 | 北京科技大学 | Strip steel hot continuous rolling quality fault diagnosis method and device |
| CN106709662A (en) * | 2016-12-30 | 2017-05-24 | 山东鲁能软件技术有限公司 | Electrical equipment operation condition classification method |
| CN108280415A (en) * | 2018-01-17 | 2018-07-13 | 武汉理工大学 | Driving behavior recognition methods based on intelligent mobile terminal |
| CN108765465A (en) * | 2018-05-31 | 2018-11-06 | 西安电子科技大学 | A kind of unsupervised SAR image change detection |
| CN109800782A (en) * | 2018-12-11 | 2019-05-24 | 国网甘肃省电力公司金昌供电公司 | A kind of electric network fault detection method and device based on fuzzy knn algorithm |
| CN110032973A (en) * | 2019-04-12 | 2019-07-19 | 哈尔滨工业大学（深圳） | A kind of unsupervised helminth classification method and system based on artificial intelligence |
| CN110163297A (en) * | 2019-05-31 | 2019-08-23 | 山东航天电子技术研究所 | A kind of method of abnormal data in diagnosis satellite telemetering data |
| CN111612033A (en) * | 2020-04-15 | 2020-09-01 | 广东电网有限责任公司 | Distribution transformer fault diagnosis method based on gravity search and density peak clustering |
| CN111860701A (en) * | 2020-09-24 | 2020-10-30 | 大唐环境产业集团股份有限公司 | Denitration system working condition discrimination preprocessing method based on clustering method |
| CN111898690A (en) * | 2020-08-05 | 2020-11-06 | 山东大学 | Power transformer fault classification method and system |
| CN112085951A (en) * | 2020-08-17 | 2020-12-15 | 西安电子科技大学 | Traffic state discrimination method, system, storage medium, computer device and application |
| CN112270355A (en) * | 2020-10-28 | 2021-01-26 | 长沙理工大学 | Active safety prediction method based on big data technology and SAE-GRU |
| CN112488242A (en) * | 2020-12-18 | 2021-03-12 | 深圳供电局有限公司 | Power metering terminal anomaly detection method and device, computer equipment and medium |
| CN112784862A (en) * | 2019-11-07 | 2021-05-11 | 中国石油化工股份有限公司 | Fault diagnosis and identification method for refining process of atmospheric and vacuum distillation unit |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100930799B1 (en) * | 2007-09-17 | 2009-12-09 | 한국전자통신연구원 | Automated Clustering Method and Multipath Clustering Method and Apparatus in Mobile Communication Environment |
| US8886574B2 (en) * | 2012-06-12 | 2014-11-11 | Siemens Aktiengesellschaft | Generalized pattern recognition for fault diagnosis in machine condition monitoring |
| WO2018223331A1 (en) * | 2017-06-08 | 2018-12-13 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for text attribute determination using conditional random field model |
| US11055318B2 (en) * | 2017-08-31 | 2021-07-06 | Intel Corporation | Target number of clusters based on internal index Fibonacci search |
-
2021
- 2021-07-28 CN CN202110857346.3A patent/CN113569950B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9336302B1 (en) * | 2012-07-20 | 2016-05-10 | Zuci Realty Llc | Insight and algorithmic clustering for automated synthesis |
| CN103150731A (en) * | 2013-03-07 | 2013-06-12 | 南京航空航天大学 | Fuzzy clustering image segmenting method |
| CN103440505A (en) * | 2013-09-16 | 2013-12-11 | 重庆邮电大学 | Spatial neighborhood information weighted hyper-spectral remote sensing image classification method |
| CN105929812A (en) * | 2016-04-18 | 2016-09-07 | 北京科技大学 | Strip steel hot continuous rolling quality fault diagnosis method and device |
| CN106709662A (en) * | 2016-12-30 | 2017-05-24 | 山东鲁能软件技术有限公司 | Electrical equipment operation condition classification method |
| CN108280415A (en) * | 2018-01-17 | 2018-07-13 | 武汉理工大学 | Driving behavior recognition methods based on intelligent mobile terminal |
| CN108765465A (en) * | 2018-05-31 | 2018-11-06 | 西安电子科技大学 | A kind of unsupervised SAR image change detection |
| CN109800782A (en) * | 2018-12-11 | 2019-05-24 | 国网甘肃省电力公司金昌供电公司 | A kind of electric network fault detection method and device based on fuzzy knn algorithm |
| CN110032973A (en) * | 2019-04-12 | 2019-07-19 | 哈尔滨工业大学（深圳） | A kind of unsupervised helminth classification method and system based on artificial intelligence |
| CN110163297A (en) * | 2019-05-31 | 2019-08-23 | 山东航天电子技术研究所 | A kind of method of abnormal data in diagnosis satellite telemetering data |
| CN112784862A (en) * | 2019-11-07 | 2021-05-11 | 中国石油化工股份有限公司 | Fault diagnosis and identification method for refining process of atmospheric and vacuum distillation unit |
| CN111612033A (en) * | 2020-04-15 | 2020-09-01 | 广东电网有限责任公司 | Distribution transformer fault diagnosis method based on gravity search and density peak clustering |
| CN111898690A (en) * | 2020-08-05 | 2020-11-06 | 山东大学 | Power transformer fault classification method and system |
| CN112085951A (en) * | 2020-08-17 | 2020-12-15 | 西安电子科技大学 | Traffic state discrimination method, system, storage medium, computer device and application |
| CN111860701A (en) * | 2020-09-24 | 2020-10-30 | 大唐环境产业集团股份有限公司 | Denitration system working condition discrimination preprocessing method based on clustering method |
| CN112270355A (en) * | 2020-10-28 | 2021-01-26 | 长沙理工大学 | Active safety prediction method based on big data technology and SAE-GRU |
| CN112488242A (en) * | 2020-12-18 | 2021-03-12 | 深圳供电局有限公司 | Power metering terminal anomaly detection method and device, computer equipment and medium |
Non-Patent Citations (3)
| Title |
|---|
| 基于粒子群优化KFCM的风电齿轮箱故障诊断;李状;柳亦兵;滕伟;林杨;;振动.测试与诊断(03);第484-488、626、627页 * |
| 朱方 等著.基于SVM的多信息融合技术在公交客流识别系统中的应用研究.东北大学出版社,2018,第53-55页. * |
| 采用FCM聚类与改进SVR模型的窃电行为检测;康宁宁;李川;曾虎;李英娜;;电子测量与仪器学报(12);第2023-2029页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113569950A (en) | 2021-10-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110245047B (en) | Time sequence abnormality detection method, device and equipment | |
| CN110262937B (en) | Identification method and device for index abnormality reasons | |
| TW201942826A (en) | Payment mode recommendation method and device and equipment | |
| CN111881973A (en) | Sample selection method and device, storage medium and electronic equipment | |
| CN110457578B (en) | Customer service demand identification method and device | |
| CN113221555B (en) | Keyword recognition method, device and equipment based on multitasking model | |
| CN113887227B (en) | Model training and entity identification method and device | |
| CN113569950B (en) | Power station equipment fault monitoring model generation method, system and device | |
| CN112417093B (en) | Model training method and device | |
| CN117390585B (en) | Time sequence data prediction method and model training method based on three-dimensional full-connection fusion | |
| CN111458030B (en) | Infrared human body temperature measurement calibration method and device | |
| CN117409466B (en) | Three-dimensional dynamic expression generation method and device based on multi-label control | |
| CN116757278B (en) | Training method and device of prediction model, storage medium and electronic equipment | |
| CN116402113B (en) | Task execution method and device, storage medium and electronic equipment | |
| CN110874652B (en) | Equipment state evaluation method, device, equipment and system | |
| CN117313141A (en) | Abnormality detection method, abnormality detection device, abnormality detection equipment and readable storage medium | |
| CN116578877A (en) | Method and device for model training and risk identification of secondary optimization marking | |
| CN111461352B (en) | Model training method, service node identification device and electronic equipment | |
| CN117932406A (en) | Training method and device for classification model applied to brain-computer interface task | |
| CN116644178A (en) | Fault identification method, device and equipment | |
| CN117035695B (en) | Information early warning method and device, readable storage medium and electronic equipment | |
| CN116699998B (en) | Unmanned on duty pressurization station control system based on AI algorithm | |
| CN117391150B (en) | Graph data retrieval model training method based on hierarchical pooling graph hash | |
| CN117393140B (en) | Intelligent finger ring control method and device based on historical data | |
| CN116432048A (en) | Abnormality detection method and device, storage medium and electronic equipment |
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 |