Thermal equipment fault analysis method and system
Through the combination of data classification, labeling and simulation model, the problem of low accuracy in extracting fault characteristics of thermal equipment is solved, more accurate fault analysis and processing is achieved, and operation and maintenance efficiency is improved.
Patent Information
- Application Number
- CN202411437868.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-14
- Publication Date
- 2025-03-21
AI Technical Summary
Due to differences in equipment types, fault types and operating environment, the accuracy of extraction of fault characteristics of thermal equipment is low.
The preset data classification model classifies the operating data of thermal equipment, and compares the classified data to obtain data category differences information. The data category distinction information is marked and matched with the thermal equipment fault analysis process information. The thermal equipment operation data corresponding to the process node with the marked data difference information is retrieved, and the thermal equipment operation data is substituted into the thermal equipment fault analysis simulation model to obtain the fault analysis simulation results, and the data of unidentified fault types are processed through the data detection and correction model.
It improves the accuracy of fault feature extraction, ensures that each process node has accurate data support, can accurately predict and identify fault types, reduces manual intervention, and improves the efficiency of fault processing.
Smart Images
Figure CN119669809A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of power plant equipment operation and maintenance, and in particular to a thermal equipment fault analysis method and system. Background Art
[0002] The application of thermal equipment failure analysis methods in thermal power plants is quite important and extensive. As a plant that produces and supplies heat and electricity at the same time, the stable operation of its core technology and equipment is crucial to ensure the supply of electricity and heat. Thermal equipment is an important part of thermal power plants. Thermal equipment in thermal power plants undertakes the important tasks of monitoring, controlling and adjusting various thermal parameters. When thermal equipment fails, it will not only affect the normal operation of the thermal power plant, but also lead to safety accidents. Therefore, it is necessary to perform fault analysis on thermal equipment to detect and handle faults in a timely manner.
[0003] Thermal equipment failures often present complex and changeable symptoms. How to accurately extract fault features from massive data is the key to fault diagnosis. However, due to differences in equipment type, fault type, and operating environment, the extraction of fault features becomes particularly difficult. In order to solve the technical pain points of thermal equipment failure analysis, it is necessary to continuously explore new technologies and methods and improve the intelligence level of the system. The present invention provides a thermal equipment failure analysis method and system. Summary of the invention
[0004] In view of the shortcomings of the prior art, the present invention provides a method and system for analyzing faults of thermal equipment to solve the problem of low accuracy in extracting fault features due to differences in equipment type, fault type and operating environment.
[0005] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows: In a first aspect, the present invention provides a method for analyzing a thermal equipment failure, comprising: Step S101, obtaining thermal equipment operation data and thermal equipment fault analysis process information, substituting the thermal equipment operation data into a preset thermal equipment fault analysis model, and obtaining the thermal equipment fault analysis result; Step S102, classifying the thermal equipment operation data through a preset data classification model to obtain first classified data thermal equipment operation data, classifying the thermal equipment fault analysis results through a preset data classification model to obtain second classified data thermal equipment operation data, and comparing the first classified data thermal equipment operation data with the second classified data thermal equipment operation data to obtain data category distinction information; Step S103, marking the data category distinction information to obtain marked data distinction information, matching the thermal equipment operation data with the thermal equipment fault analysis process information to obtain the thermal equipment operation data of each process node, retrieving the marked data distinction information from the thermal equipment operation data of each process node to obtain the process node with the marked data distinction information; Step S104, retrieve the thermal equipment operation data corresponding to the process node with the marked data distinction information, obtain the data to be detected, substitute the data to be detected into the thermal equipment fault analysis simulation model, and obtain the thermal equipment fault analysis simulation result, the thermal equipment fault analysis simulation result includes that the thermal equipment has no fault and the thermal equipment has a fault, and the thermal equipment has a fault including an identifiable thermal equipment fault type and an unidentifiable thermal equipment fault type; Step S105, detecting the data corresponding to the unrecognizable thermal equipment fault type through a preset data detection model to obtain a data error detection result, matching the data error detection result in a preset data correction knowledge base to obtain a data repair strategy corresponding to the data error detection result, using the data repair strategy corresponding to the data error detection result to correct the data corresponding to the unrecognizable thermal equipment fault type to obtain corrected data, and replacing the data corresponding to the unrecognizable thermal equipment fault type with the corrected data.
[0006] Furthermore, the thermal equipment fault analysis method provided by the present invention, step S103 includes: Thermal equipment operation data includes equipment parameters and equipment status information during operation; Marking the data category distinction information in the thermal equipment operation data according to the data category distinction information obtained in the data comparison unit; Extract features of thermal equipment failure analysis process information to obtain each node of the thermal equipment failure analysis process; According to each node of the fault analysis process, filter out the data matching each node from the thermal equipment operation data; For each process node, the data corresponding to each process node is integrated into a data set, and the data set includes all the operation data required by the node; Verify the generated data set to check whether the data set contains the previously marked data category distinction information.
[0007] Furthermore, in the thermal equipment fault analysis method provided by the present invention, step S104 comprises: According to the data distinction information marked in the data matching unit, the data to be tested is extracted from the operation data of the thermal equipment corresponding to each process node; Preprocess the extracted data to be tested, match the corresponding fault analysis simulation model according to the type of thermal equipment and the requirements of fault analysis, and substitute the preprocessed data to be tested into the corresponding fault analysis simulation model; Start the fault analysis simulation model, perform fault analysis simulation on the input data, and obtain the fault analysis simulation results; For identifiable fault types, data repair is performed according to a data repair strategy corresponding to the identifiable fault type.
[0008] Furthermore, the thermal equipment fault analysis method provided by the present invention, step S105 includes: The operation data corresponding to the unrecognizable thermal equipment fault type are grouped according to the process node to obtain the unrecognizable thermal equipment fault type grouped data, the unrecognizable thermal equipment fault type grouped data are classified, the classified unrecognizable thermal equipment fault type grouped data are compared with the preset standard value to obtain the comparison result of the classified unrecognizable thermal equipment fault type grouped data, the classified unrecognizable thermal equipment fault type grouped data inconsistent with the preset standard number are marked as abnormal data, the abnormal data are clustered and analyzed to obtain the cluster analysis result, the cluster analysis result is matched in the preset data correction knowledge base, and according to the similarity between the fault type in the preset data correction knowledge base and the cluster analysis result, the fault type with the highest similarity in the preset data correction knowledge base is selected as the fault cause.
[0009] In a second aspect, the present invention provides a thermal equipment failure analysis system, which is applied to the thermal equipment failure analysis method as described above, comprising: A data acquisition unit is used to acquire the operation data of thermal equipment and the failure analysis process information of thermal equipment, substitute the operation data of thermal equipment into the preset failure analysis model of thermal equipment, and obtain the failure analysis result of thermal equipment; A data comparison unit is used to classify the thermal equipment operation data through a preset data classification model to obtain first classified data thermal equipment operation data, classify the thermal equipment fault analysis results through a preset data classification model to obtain second classified data thermal equipment operation data, and compare the first classified data thermal equipment operation data with the second classified data thermal equipment operation data to obtain data category distinction information; The data matching unit is used to mark the data category distinction information to obtain the marked data distinction information, match the thermal equipment operation data with the thermal equipment fault analysis process information to obtain the thermal equipment operation data of each process node, retrieve the marked data distinction information from the thermal equipment operation data of each process node, and obtain the process node with the marked data distinction information; A data detection unit is used to retrieve the thermal equipment operation data corresponding to the process node with the marked data distinction information, obtain the data to be detected, substitute the data to be detected into the thermal equipment fault analysis simulation model, and obtain the thermal equipment fault analysis simulation result, the thermal equipment fault analysis simulation result includes that the thermal equipment has no fault and the thermal equipment has a fault, and the thermal equipment has a fault including an identifiable thermal equipment fault type and an unidentifiable thermal equipment fault type; The error data correction unit is used to detect the data corresponding to the unrecognizable thermal equipment fault type through a preset data detection model to obtain a data error detection result, match the data error detection result in a preset data correction knowledge base to obtain a data repair strategy corresponding to the data error detection result, use the data repair strategy corresponding to the data error detection result to correct the data corresponding to the unrecognizable thermal equipment fault type to obtain corrected data, and replace the data corresponding to the unrecognizable thermal equipment fault type with the corrected data.
[0010] Furthermore, in the thermal equipment fault analysis system provided by the present invention, the data matching unit is also used for: Thermal equipment operation data includes equipment parameters and equipment status information during operation; Marking the data category distinction information in the thermal equipment operation data according to the data category distinction information obtained in the data comparison unit; Extract features of thermal equipment failure analysis process information to obtain each node of the thermal equipment failure analysis process; According to each node of the fault analysis process, filter out the data matching each node from the thermal equipment operation data; For each process node, the data corresponding to each process node is integrated into a data set, and the data set includes all the operation data required by the node; Verify the generated data set to check whether the data set contains the previously marked data category distinction information.
[0011] Furthermore, in the thermal equipment fault analysis system provided by the present invention, the data detection unit is also used for: Step S104, retrieve the thermal equipment operation data corresponding to the process node with the marked data distinction information, obtain the data to be detected, substitute the data to be detected into the thermal equipment fault analysis simulation model, and obtain the thermal equipment fault analysis simulation result, the thermal equipment fault analysis simulation result includes that the thermal equipment has no fault and the thermal equipment has a fault, and the thermal equipment has a fault including an identifiable thermal equipment fault type and an unidentifiable thermal equipment fault type.
[0012] Furthermore, in the thermal equipment fault analysis system provided by the present invention, the error data correction unit is further used for: The operation data corresponding to the unrecognizable thermal equipment fault type are grouped according to the process node to obtain the unrecognizable thermal equipment fault type grouped data, the unrecognizable thermal equipment fault type grouped data are classified, the classified unrecognizable thermal equipment fault type grouped data are compared with the preset standard value to obtain the comparison result of the classified unrecognizable thermal equipment fault type grouped data, the classified unrecognizable thermal equipment fault type grouped data inconsistent with the preset standard number are marked as abnormal data, the abnormal data are clustered and analyzed to obtain the cluster analysis result, the cluster analysis result is matched in the preset data correction knowledge base, and according to the similarity between the fault type in the preset data correction knowledge base and the cluster analysis result, the fault type with the highest similarity in the preset data correction knowledge base is selected as the fault cause.
[0013] The beneficial effects of the present invention are mainly reflected in the following aspects: The thermal equipment operation data and fault analysis results are classified through the preset data classification model, and the classified data are compared to obtain data category distinction information. This method can effectively identify the differences between different categories of data, thereby improving the accuracy of fault feature extraction.
[0014] The data category distinction information is marked and matched with the thermal equipment fault analysis process information to obtain the process node with the marked data distinction information. This ensures that each process node has accurate data support and improves the pertinence of fault analysis.
[0015] The thermal equipment fault analysis simulation model can predict and identify the fault types of thermal equipment, including identifiable fault types and unidentifiable fault types. This method not only covers common fault types, but also can make preliminary judgments on complex or unknown faults.
[0016] For thermal equipment fault types that cannot be identified in the simulation results, they are detected through the preset data detection model, and the corresponding data correction strategy is found in the data correction knowledge base for correction. This realizes the automatic detection and correction of erroneous data, reduces manual intervention, and improves the efficiency of fault handling.
[0017] The present invention constructs an intelligent thermal equipment fault analysis system by integrating multiple modules such as data classification, data tagging, fault analysis simulation, data detection and correction. The present invention can automatically process a large amount of data, quickly and accurately identify the fault type, and provide strong support for the operation and maintenance of thermal equipment.
[0018] By improving the accuracy and efficiency of fault analysis, the present invention can help operation and maintenance personnel locate and solve fault problems more quickly, thereby reducing equipment downtime and maintenance costs. At the same time, automated data processing and fault analysis also reduce the degree of dependence on professionals, further reducing operation and maintenance costs.
[0019] In summary, the present invention effectively improves the accuracy and efficiency of thermal equipment fault analysis through a series of innovative technical means, and provides strong technical support for equipment operation and maintenance in industrial fields such as thermal power plants. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.
[0021] Figure 1 A schematic diagram of a thermal equipment failure analysis method provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0022] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.
[0023] In order to better understand the purpose of the present invention, the present invention is described in further detail below.
[0024] In a first aspect, the present invention provides a method for analyzing a thermal equipment failure, comprising: Step S101, obtaining thermal equipment operation data and thermal equipment fault analysis process information, substituting the thermal equipment operation data into a preset thermal equipment fault analysis model, and obtaining the thermal equipment fault analysis result; In step S101, the thermal equipment failure analysis method of the present invention first performs the following operations: Collect various data generated by thermal equipment during operation, including equipment operating parameters (such as temperature, pressure, flow, etc.) and equipment status information (such as switch status, operating mode, etc.).
[0025] In addition to operating data, process information related to thermal equipment failure analysis also needs to be obtained. The process information related to thermal equipment failure analysis describes the steps and sequence to be followed during the failure analysis process, as well as the specific data or operations required for each step. Process information helps improve the systematicness and accuracy of failure analysis.
[0026] Substitute the collected thermal equipment operation data into a preset fault analysis model. The preset fault analysis model is established based on a deep understanding of historical fault data, equipment characteristics, and fault mechanisms. The preset fault analysis model can use the input operation data to infer the fault status or potential problems of the equipment through specific algorithms or logic.
[0027] After being processed by the fault analysis model, step S101 finally outputs the fault analysis result of the thermal equipment. The fault analysis result of the thermal equipment includes information such as whether the equipment has a fault, the specific type of the fault, the location of the fault, and the severity of the fault.
[0028] In summary, step S101 collects the operation data and fault analysis process information of the thermal equipment and processes the data using a preset fault analysis model, thereby providing important input and basis for subsequent fault feature extraction and fault type identification.
[0029] Step S102, classifying the thermal equipment operation data through a preset data classification model to obtain first classified data thermal equipment operation data, classifying the thermal equipment fault analysis results through a preset data classification model to obtain second classified data thermal equipment operation data, and comparing the first classified data thermal equipment operation data with the second classified data thermal equipment operation data to obtain data category distinction information; In step S102, the thermal equipment fault analysis method of the present invention further refines the data processing process to improve the accuracy and efficiency of fault feature extraction. The specific execution steps are as follows: First, the operating data of thermal equipment is classified through a preset data classification model. The data classification model is designed based on the type, source, importance or other relevant characteristics of the data. The purpose of classification is to divide the huge operating data set into small subsets that are easier to manage and analyze.
[0030] Similarly, the thermal equipment failure analysis results are also classified through the same data classification model. This can make the analysis results consistent with the original operation data in the classification dimension, which is convenient for subsequent comparison and analysis.
[0031] After being processed by the data classification model, the thermal equipment operation data is divided into the first classified data set, and the fault analysis results are divided into the second classified data set. These two data sets represent different categories of information extracted from the original data and the analysis results, respectively.
[0032] Next, the thermal equipment operation data after the first classification is compared with the thermal equipment operation data after the second classification. The purpose of this step is to identify the differences and similarities between the two, especially the data category distinction information that affects the accuracy of fault analysis.
[0033] By comparing, we can find out which data categories play a role in the fault analysis process and which data categories contain redundant or misleading information. This information is used for subsequent data processing and fault feature extraction.
[0034] Finally, step S102 outputs data category distinction information, which describes in detail the differences and connections between different data categories and their impact on the fault analysis results. The data category distinction information provides an important reference and basis for subsequent steps, and helps to more accurately identify fault characteristics and types.
[0035] In summary, step S102 effectively extracts the data category distinction information between the thermal equipment operation data and the fault analysis results through the data classification and comparison method, laying a solid foundation for the subsequent fault feature extraction and fault type identification.
[0036] Step S103, marking the data category distinction information to obtain marked data distinction information, matching the thermal equipment operation data with the thermal equipment fault analysis process information to obtain the thermal equipment operation data of each process node, retrieving the marked data distinction information from the thermal equipment operation data of each process node to obtain the process node with the marked data distinction information; In step S103, the thermal equipment fault analysis method of the present invention further refines the data processing flow to improve the accuracy and efficiency of fault analysis. The specific execution process of this step is as follows: First, the data category difference information obtained in step S102 is marked. The purpose of this step is to highlight the data category differences that are important in the fault analysis process so that the subsequent steps can more easily identify and process this information.
[0037] The tagging process involves assigning unique identifiers, color coding, or other visual cues to different data categories to distinguish information for easy differentiation and tracking.
[0038] Next, the operation data of the thermal equipment is matched with the thermal equipment fault analysis process information. The purpose of this step is to map the operation data to each process node of the fault analysis so that each node has corresponding data support. The matching process involves parsing the fault analysis process information, extracting the data requirements of each node, and then searching and extracting the data that meets the requirements in the operation data set.
[0039] Through the matching process, the thermal equipment operation data corresponding to each process node is obtained. The data set represents the specific information required at different fault analysis stages and provides important input for subsequent steps.
[0040] Retrieve previously marked data category distinction information from the thermal equipment operation data at each process node. The purpose of this step is to take into account those data category differences that are of great significance at each stage of fault analysis.
[0041] The retrieval process involves searching a data collection for specific identifiers, color codes, or other visual cues to locate the labeled data.
[0042] Finally, step S103 outputs a process node with marked data difference information. The process node not only contains the original operation data, but also highlights the data category differences that are important in the fault analysis process, providing richer and more accurate input for subsequent steps, which helps to improve the accuracy and efficiency of fault analysis.
[0043] In summary, step S103 further refines the data processing process by marking the data category distinction information, matching the operating data with the fault analysis process information, and retrieving the marked information, providing more accurate and rich input for subsequent fault feature extraction and fault type identification.
[0044] Step S104, retrieve the thermal equipment operation data corresponding to the process node with the marked data distinction information, obtain the data to be detected, substitute the data to be detected into the thermal equipment fault analysis simulation model, and obtain the thermal equipment fault analysis simulation result, the thermal equipment fault analysis simulation result includes that the thermal equipment has no fault and the thermal equipment has a fault, and the thermal equipment has a fault including an identifiable thermal equipment fault type and an unidentifiable thermal equipment fault type; In step S104, the thermal equipment fault analysis method of the present invention further deepens the fault analysis process and predicts and identifies the fault type through the simulation model. The following are the specific execution details of this step: The thermal equipment operation data corresponding to the process node with the marked data distinction information is retrieved. The system extracts the thermal equipment operation data corresponding to the process node with the marked data distinction information from the previously processed data.
[0045] The system takes the data as the data to be tested and prepares to input it into the subsequent fault analysis simulation model. The quality of the data to be tested directly affects the accuracy of the fault analysis results.
[0046] Next, the system will substitute the data to be tested into the preset thermal equipment failure analysis simulation model. The thermal equipment failure analysis simulation model is built based on a large amount of historical data and expert knowledge, and can simulate the operation process of thermal equipment and predict failure conditions.
[0047] By running the fault analysis simulation model, the system can output the fault analysis simulation results of the thermal equipment. The fault analysis simulation results of the thermal equipment include two types: the thermal equipment has no fault or the thermal equipment has a fault.
[0048] When a fault exists, it is further classified. If the simulation results show that there is a fault in the thermal equipment, the system will further subdivide the fault type into identifiable thermal equipment fault type and unidentifiable thermal equipment fault type. Identifiable fault types refer to those faults that the system can directly identify and give clear repair suggestions, while unidentifiable fault types refer to those faults that the system cannot accurately identify temporarily or require more in-depth analysis.
[0049] Through step S104, the method of the present invention can effectively use the thermal equipment operation data with the marked data distinction information to predict and identify the fault conditions of the thermal equipment through the simulation model. This method not only improves the accuracy of fault analysis, but also helps maintenance personnel locate and solve fault problems more quickly.
[0050] Step S105, detecting the data corresponding to the unrecognizable thermal equipment fault type through a preset data detection model to obtain a data error detection result, matching the data error detection result in a preset data correction knowledge base to obtain a data repair strategy corresponding to the data error detection result, using the data repair strategy corresponding to the data error detection result to correct the data corresponding to the unrecognizable thermal equipment fault type to obtain corrected data, and replacing the data corresponding to the unrecognizable thermal equipment fault type with the corrected data.
[0051] In step S105, the present invention provides an in-depth processing mechanism for unidentifiable thermal equipment fault types, aiming to improve the accuracy and completeness of fault analysis through data detection and correction. The following is a detailed explanation of this step: For the data corresponding to the unrecognizable thermal equipment fault type identified in step S104, the system first detects it through a preset data detection model. The data detection model is constructed based on machine learning or statistical methods and can identify abnormal or error patterns in the data.
[0052] Through the data detection model, the system can obtain a series of data error detection results. The data error detection results include outliers, missing values, inconsistencies or other types of errors in the data.
[0053] Next, the system matches the data error detection results with the preset data correction knowledge base, which is a database that stores various known fault types and their corresponding data repair strategies.
[0054] Through the matching process, the system can find the data repair strategy that best matches the current data error detection result. This strategy includes data replacement, interpolation, smoothing or other types of correction methods.
[0055] When a suitable data repair strategy is matched, the system will use this strategy to correct the data corresponding to the unrecognizable thermal equipment fault type. This process involves modifying or replacing the original data to improve the accuracy and consistency of the data.
[0056] Finally, the system replaces the corrected data back to the position corresponding to the unrecognizable thermal equipment fault type in the original data. In this way, the corrected data can be used for subsequent fault analysis or other related processing, thereby improving the accuracy and reliability of the entire system.
[0057] Through step S105, the method of the present invention can effectively handle those thermal equipment fault types that cannot be identified initially, and improve the accuracy and completeness of fault analysis through data detection and correction. This method not only improves the intelligence level of the system, but also improves the efficiency and accuracy of fault handling.
[0058] Specifically, the thermal equipment fault analysis method provided by the present invention, step S103 includes: Thermal equipment operation data includes equipment parameters and equipment status information during operation; Marking the data category distinction information in the thermal equipment operation data according to the data category distinction information obtained in the data comparison unit; Extract features of thermal equipment failure analysis process information to obtain each node of the thermal equipment failure analysis process; According to each node of the fault analysis process, filter out the data matching each node from the thermal equipment operation data; For each process node, the data corresponding to each process node is integrated into a data set, and the data set includes all the operation data required by the node; Verify the generated data set to check whether the data set contains the previously marked data category distinction information.
[0059] Specifically, the thermal equipment fault analysis method provided by the present invention, step S104 includes: According to the data distinction information marked in the data matching unit, the data to be tested is extracted from the operation data of the thermal equipment corresponding to each process node; Preprocess the extracted data to be tested, match the corresponding fault analysis simulation model according to the type of thermal equipment and the requirements of fault analysis, and substitute the preprocessed data to be tested into the corresponding fault analysis simulation model; Start the fault analysis simulation model, perform fault analysis simulation on the input data, and obtain the fault analysis simulation results; For identifiable fault types, data repair is performed according to a data repair strategy corresponding to the identifiable fault type.
[0060] Specifically, the thermal equipment fault analysis method provided by the present invention, step S105 includes: The operation data corresponding to the unrecognizable thermal equipment fault type are grouped according to the process node to obtain the unrecognizable thermal equipment fault type grouped data, the unrecognizable thermal equipment fault type grouped data are classified, the classified unrecognizable thermal equipment fault type grouped data are compared with the preset standard value to obtain the comparison result of the classified unrecognizable thermal equipment fault type grouped data, the classified unrecognizable thermal equipment fault type grouped data inconsistent with the preset standard number are marked as abnormal data, the abnormal data are clustered and analyzed to obtain the cluster analysis result, the cluster analysis result is matched in the preset data correction knowledge base, and according to the similarity between the fault type in the preset data correction knowledge base and the cluster analysis result, the fault type with the highest similarity in the preset data correction knowledge base is selected as the fault cause.
[0061] In a second aspect, the present invention provides a thermal equipment failure analysis system, which is applied to the thermal equipment failure analysis method as described above, comprising: A data acquisition unit is used to acquire the operation data of thermal equipment and the failure analysis process information of thermal equipment, substitute the operation data of thermal equipment into the preset failure analysis model of thermal equipment, and obtain the failure analysis result of thermal equipment; A data comparison unit is used to classify the thermal equipment operation data through a preset data classification model to obtain first classified data thermal equipment operation data, classify the thermal equipment fault analysis results through a preset data classification model to obtain second classified data thermal equipment operation data, and compare the first classified data thermal equipment operation data with the second classified data thermal equipment operation data to obtain data category distinction information; The data matching unit is used to mark the data category distinction information to obtain the marked data distinction information, match the thermal equipment operation data with the thermal equipment fault analysis process information to obtain the thermal equipment operation data of each process node, retrieve the marked data distinction information from the thermal equipment operation data of each process node, and obtain the process node with the marked data distinction information; A data detection unit is used to retrieve the thermal equipment operation data corresponding to the process node with the marked data distinction information, obtain the data to be detected, substitute the data to be detected into the thermal equipment fault analysis simulation model, and obtain the thermal equipment fault analysis simulation result, the thermal equipment fault analysis simulation result includes that the thermal equipment has no fault and the thermal equipment has a fault, and the thermal equipment has a fault including an identifiable thermal equipment fault type and an unidentifiable thermal equipment fault type; The error data correction unit is used to detect the data corresponding to the unrecognizable thermal equipment fault type through a preset data detection model to obtain a data error detection result, match the data error detection result in a preset data correction knowledge base to obtain a data repair strategy corresponding to the data error detection result, use the data repair strategy corresponding to the data error detection result to correct the data corresponding to the unrecognizable thermal equipment fault type to obtain corrected data, and replace the data corresponding to the unrecognizable thermal equipment fault type with the corrected data.
[0062] Specifically, in the thermal equipment fault analysis system provided by the present invention, the data matching unit is further used for: Thermal equipment operation data includes equipment parameters and equipment status information during operation; Marking the data category distinction information in the thermal equipment operation data according to the data category distinction information obtained in the data comparison unit; Extract features of thermal equipment failure analysis process information to obtain each node of the thermal equipment failure analysis process; According to each node of the fault analysis process, filter out the data matching each node from the thermal equipment operation data; For each process node, the data corresponding to each process node is integrated into a data set, and the data set includes all the operation data required by the node; Verify the generated data set to check whether the data set contains the previously marked data category distinction information.
[0063] Specifically, in the thermal equipment fault analysis system provided by the present invention, the data detection unit is further used for: Retrieve the thermal equipment operation data corresponding to the process node with marked data distinction information to obtain the data to be tested, substitute the data to be tested into the thermal equipment fault analysis simulation model to obtain the thermal equipment fault analysis simulation results, the thermal equipment fault analysis simulation results include thermal equipment without faults, thermal equipment faults with faults, thermal equipment faults with faults include identifiable thermal equipment fault types and unidentifiable thermal equipment fault types.
[0064] Specifically, in the thermal equipment fault analysis system provided by the present invention, the error data correction unit is further used for: The operation data corresponding to the unrecognizable thermal equipment fault type are grouped according to the process node to obtain the unrecognizable thermal equipment fault type grouped data, the unrecognizable thermal equipment fault type grouped data are classified, the classified unrecognizable thermal equipment fault type grouped data are compared with the preset standard value to obtain the comparison result of the classified unrecognizable thermal equipment fault type grouped data, the classified unrecognizable thermal equipment fault type grouped data inconsistent with the preset standard number are marked as abnormal data, the abnormal data are clustered and analyzed to obtain the cluster analysis result, the cluster analysis result is matched in the preset data correction knowledge base, and according to the similarity between the fault type in the preset data correction knowledge base and the cluster analysis result, the fault type with the highest similarity in the preset data correction knowledge base is selected as the fault cause.
[0065] The present invention provides a method and system for analyzing faults of thermal equipment, aiming to solve the problem of low accuracy of fault feature extraction due to differences in equipment type, fault type and operating environment. Specifically, the present invention achieves this goal through the following technical means: The thermal equipment operation data is classified by the preset data classification model to obtain the first classified data. The thermal equipment fault analysis results are also classified by the same data classification model to obtain the second classified data. The data after the first classification and the data after the second classification are compared to obtain the data category difference information. This step helps to identify the differences between different categories of data and improve the accuracy of data processing.
[0066] Mark the data category distinction information, and then match the thermal equipment operation data with the thermal equipment fault analysis process information. Retrieve the marked data distinction information from the thermal equipment operation data of each process node to obtain the process node with the marked data distinction information. This step ensures that each process node has accurate data support and improves the pertinence of fault analysis.
[0067] The thermal equipment operation data corresponding to the process node with the marked data distinction information is retrieved as the data to be tested. The data to be tested is substituted into the thermal equipment fault analysis simulation model to obtain the fault analysis simulation results. This step uses the simulation model to simulate and analyze the actual data, which can more accurately predict and identify the fault type.
[0068] For the thermal equipment fault types that cannot be identified in the simulation results, the preset data detection model is used to detect and obtain the data error detection results. The data error detection results are matched in the preset data correction knowledge base to obtain the corresponding data repair strategy, and the data corresponding to the unidentifiable fault type is corrected. This step improves the accuracy of fault feature extraction through data detection and correction, and solves the problems caused by differences in equipment, fault types and operating environments.
[0069] In summary, the present invention effectively improves the accuracy of extracting fault characteristics of thermal equipment through steps such as data classification and comparison, data marking and matching, fault analysis simulation, and error data detection and correction, and solves the problems caused by differences in equipment type, fault type, and operating environment.
[0070] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations, and the above-described embodiments of the present invention do not constitute a limitation on the scope of protection of the present invention.
Claims
1. A method for analyzing failure of thermal equipment, characterized in that: include: Step S101, obtaining thermal equipment operation data and thermal equipment fault analysis process information, substituting the thermal equipment operation data into a preset thermal equipment fault analysis model, and obtaining the thermal equipment fault analysis result; Step S102, classifying the thermal equipment operation data through a preset data classification model to obtain first classified data thermal equipment operation data, classifying the thermal equipment fault analysis results through a preset data classification model to obtain second classified data thermal equipment operation data, and comparing the first classified data thermal equipment operation data with the second classified data thermal equipment operation data to obtain data category distinction information; Step S103, marking the data category distinction information to obtain marked data distinction information, matching the thermal equipment operation data with the thermal equipment fault analysis process information to obtain the thermal equipment operation data of each process node, retrieving the marked data distinction information from the thermal equipment operation data of each process node to obtain the process node with the marked data distinction information; Step S104, retrieve the thermal equipment operation data corresponding to the process node with the marked data distinction information, obtain the data to be detected, substitute the data to be detected into the thermal equipment fault analysis simulation model, and obtain the thermal equipment fault analysis simulation result, the thermal equipment fault analysis simulation result includes that the thermal equipment has no fault and the thermal equipment has a fault, and the thermal equipment has a fault including an identifiable thermal equipment fault type and an unidentifiable thermal equipment fault type; Step S105, detecting the data corresponding to the unrecognizable thermal equipment fault type through a preset data detection model to obtain a data error detection result, matching the data error detection result in a preset data correction knowledge base to obtain a data repair strategy corresponding to the data error detection result, using the data repair strategy corresponding to the data error detection result to correct the data corresponding to the unrecognizable thermal equipment fault type to obtain corrected data, and replacing the data corresponding to the unrecognizable thermal equipment fault type with the corrected data.
2. The thermal equipment failure analysis method according to claim 1, characterized in that: The step S103 includes: Thermal equipment operation data includes equipment parameters and equipment status information during operation; Marking the data category distinction information in the thermal equipment operation data according to the data category distinction information obtained in the data comparison unit; Feature extraction is performed on the thermal equipment fault analysis process information to obtain each node of the thermal equipment fault analysis process; According to each node of the fault analysis process, filter out the data matching each node from the thermal equipment operation data; For each process node, the data corresponding to each process node is integrated into a data set, and the data set includes all the operation data required by the node; Verify the generated data set to check whether the data set contains the previously marked data category distinction information.
3. The thermal equipment failure analysis method according to claim 1, characterized in that: The step S104 includes: According to the data distinction information marked in the data matching unit, the data to be tested is extracted from the operation data of the thermal equipment corresponding to each process node; Preprocess the extracted data to be tested, match the corresponding fault analysis simulation model according to the type of thermal equipment and the requirements of fault analysis, and substitute the preprocessed data to be tested into the corresponding fault analysis simulation model; Start the fault analysis simulation model, perform fault analysis simulation on the input data, and obtain the fault analysis simulation results; For identifiable fault types, data repair is performed according to a data repair strategy corresponding to the identifiable fault type.
4. The thermal equipment failure analysis method according to claim 1, characterized in that: The step S105 includes: The operation data corresponding to the unrecognizable thermal equipment fault type are grouped according to the process node to obtain the unrecognizable thermal equipment fault type grouped data, the unrecognizable thermal equipment fault type grouped data are classified, the classified unrecognizable thermal equipment fault type grouped data are compared with the preset standard value to obtain the comparison result of the classified unrecognizable thermal equipment fault type grouped data, the classified unrecognizable thermal equipment fault type grouped data inconsistent with the preset standard number are marked as abnormal data, the abnormal data are clustered and analyzed to obtain the cluster analysis result, the cluster analysis result is matched in the preset data correction knowledge base, and according to the similarity between the fault type in the preset data correction knowledge base and the cluster analysis result, the fault type with the highest similarity in the preset data correction knowledge base is selected as the fault cause.
5. A thermal equipment failure analysis system, applied to the thermal equipment failure analysis method according to any one of claims 1 to 4, characterized in that: include: A data acquisition unit is used to acquire the operation data of thermal equipment and the failure analysis process information of thermal equipment, substitute the operation data of thermal equipment into the preset failure analysis model of thermal equipment, and obtain the failure analysis result of thermal equipment; A data comparison unit is used to classify the thermal equipment operation data through a preset data classification model to obtain first classified data thermal equipment operation data, classify the thermal equipment fault analysis results through a preset data classification model to obtain second classified data thermal equipment operation data, and compare the first classified data thermal equipment operation data with the second classified data thermal equipment operation data to obtain data category distinction information; The data matching unit is used to mark the data category distinction information to obtain the marked data distinction information, match the thermal equipment operation data with the thermal equipment fault analysis process information to obtain the thermal equipment operation data of each process node, retrieve the marked data distinction information from the thermal equipment operation data of each process node, and obtain the process node with the marked data distinction information; A data detection unit is used to retrieve the thermal equipment operation data corresponding to the process node with the marked data distinction information, obtain the data to be detected, substitute the data to be detected into the thermal equipment fault analysis simulation model, and obtain the thermal equipment fault analysis simulation result, the thermal equipment fault analysis simulation result includes that the thermal equipment has no fault and the thermal equipment has a fault, and the thermal equipment has a fault including an identifiable thermal equipment fault type and an unidentifiable thermal equipment fault type; The error data correction unit is used to detect the data corresponding to the unrecognizable thermal equipment fault type through a preset data detection model to obtain a data error detection result, match the data error detection result in a preset data correction knowledge base to obtain a data repair strategy corresponding to the data error detection result, use the data repair strategy corresponding to the data error detection result to correct the data corresponding to the unrecognizable thermal equipment fault type to obtain corrected data, and replace the data corresponding to the unrecognizable thermal equipment fault type with the corrected data.
6. The thermal equipment failure analysis system according to claim 5, characterized in that: The data matching unit is further used for: Thermal equipment operation data includes equipment parameters and equipment status information during operation; Marking the data category distinction information in the thermal equipment operation data according to the data category distinction information obtained in the data comparison unit; Feature extraction is performed on the thermal equipment fault analysis process information to obtain each node of the thermal equipment fault analysis process; According to each node of the fault analysis process, filter out the data matching each node from the thermal equipment operation data; For each process node, the data corresponding to each process node is integrated into a data set, and the data set includes all the operation data required by the node; Verify the generated data set to check whether the data set contains the previously marked data category distinction information.
7. The thermal equipment failure analysis system according to claim 5, characterized in that: The data detection unit is further used for: According to the data distinction information marked in the data matching unit, the data to be tested is extracted from the operation data of the thermal equipment corresponding to each process node; Preprocess the extracted data to be tested, match the corresponding fault analysis simulation model according to the type of thermal equipment and the requirements of fault analysis, and substitute the preprocessed data to be tested into the corresponding fault analysis simulation model; Start the fault analysis simulation model, perform fault analysis simulation on the input data, and obtain the fault analysis simulation results; For identifiable fault types, data repair is performed according to a data repair strategy corresponding to the identifiable fault type.
8. The thermal equipment failure analysis system according to claim 5, characterized in that: The error data correction unit is further used for: The operation data corresponding to the unrecognizable thermal equipment fault type are grouped according to the process node to obtain the unrecognizable thermal equipment fault type grouped data, the unrecognizable thermal equipment fault type grouped data are classified, the classified unrecognizable thermal equipment fault type grouped data are compared with the preset standard value to obtain the comparison result of the classified unrecognizable thermal equipment fault type grouped data, the classified unrecognizable thermal equipment fault type grouped data inconsistent with the preset standard number are marked as abnormal data, the abnormal data are clustered and analyzed to obtain the cluster analysis result, the cluster analysis result is matched in the preset data correction knowledge base, and according to the similarity between the fault type in the preset data correction knowledge base and the cluster analysis result, the fault type with the highest similarity in the preset data correction knowledge base is selected as the fault cause.