Health management method for thermal power generation equipment

Abstract

The invention discloses a thermal power generation equipment health management method, and belongs to the technical field of thermal power generation equipment health management, and the method comprises the following steps: S1, regularly collecting historical operation data of equipment; s2, preprocessing the collected historical operation data to obtain a historical data sample; s3, constructing a neural network fault prediction model, and performing training and testing; s4, performing fault prediction in real time by using the neural network fault prediction model; s5, performing risk level division and key level division on different devices, and performing fusion calculation to obtain health indexes of the devices; and S6, predicting the fault development trend of the equipment according to the health index of each piece of equipment, and timely arranging maintenance at the early stage of the fault. According to the health management method for the thermal power generation equipment, the key equipment and the high-risk equipment can be overhauled and maintained in time, the maintenance sequence and mode are effectively and reasonably arranged, and resource waste caused by excessive maintenance is avoided.

Description

Technical Field

[0001] This invention relates to the field of health management technology for thermal power generation equipment, and in particular to a health management method for thermal power generation equipment. Background Technology

[0002] In existing technologies, smart thermal power plants are based on the development of digital and information technologies. They integrate advanced sensing and measurement, information communication, automatic control, artificial intelligence, cloud computing, big data processing, and 3D visualization, among other information technologies. These technologies are combined with the industrial technologies in traditional power generation processes, with a focus on building an integrated data flow system and a comprehensive multi-system linkage application platform, aiming to create a digital management hub for thermal power plants—a "digital brain."

[0003] Through this platform, smart thermal power plants can facilitate seamless information exchange, enabling proactive assessment and handling of potential problems, allowing data to self-evolve, and supporting automated and interconnected operations, further deepening integration with the power plant's physical infrastructure. The goal is to improve equipment reliability, optimize operational performance and power generation efficiency, reduce energy consumption and environmental pollution, enhance adaptability to the electricity market, and lower overall operating costs. Ultimately, smart thermal power plants will exhibit advanced characteristics in intelligence, safety, economic efficiency, and environmental protection, forming a new type of modern thermal power generation model.

[0004] In contrast, traditional health management of thermal power generation equipment mainly relies on manual monitoring and regular maintenance, which has many drawbacks. On the one hand, it is inefficient and cannot meet the high requirements of modern power production; on the other hand, it cannot achieve real-time fault prediction and prevention, increasing the risk of equipment failure and potentially leading to production interruptions and economic losses. Summary of the Invention

[0005] The purpose of this invention is to provide a health management method for thermal power generation equipment, which can promptly inspect and maintain key and high-risk equipment, reduce the failure rate of key equipment, ensure the normal operation of thermal power generation system, effectively achieve reasonable arrangement of maintenance sequence and method, avoid resource waste caused by excessive maintenance, and balance maintenance costs.

[0006] To achieve the above objectives, the present invention provides a health management method for thermal power generation equipment, comprising the following steps: S1. Periodically collect historical operating data of the equipment; S2. Preprocess the collected historical operational data and classify the preprocessed data into health datasets and fault datasets, and use them as historical data samples. S3. Construct a neural network fault prediction model and train and test the neural network fault prediction model based on historical data samples. S4. Use the trained neural network fault prediction model to collect equipment operating data in real time for fault prediction. S5. Based on the prediction results of historical operation data, the risk level of the operation status of different equipment is divided into risk levels, and the critical level is divided according to the importance of different equipment. The failure risk level and critical level of the same equipment are integrated and calculated to obtain the health index of the equipment. Based on the health index, the critical equipment with high risk level is prioritized for maintenance. S6. For the health index of each piece of equipment, use multiple regression analysis to predict the failure development trend of the equipment and arrange maintenance in the early stage of failure.

[0007] Preferably, in S2, the preprocessing involves cleaning the historical operating data and extracting feature information from the historical operating data.

[0008] Preferably, in S3, the health dataset and the fault dataset are divided into training set and test set respectively, and a neural network fault prediction model is constructed; the training set is imported into the neural network fault prediction model for training, which is used to establish the feature layer of the neural network fault prediction model and output the training results; then the test set is imported into the neural network fault prediction model for testing and the test results are output; by comparing the test results with the training results, the accuracy of the output results of the neural network fault prediction model is verified.

[0009] Preferably, in S5, the fault prediction results of the neural network fault prediction model are divided into categories based on the fault prediction results of different devices, with higher fault risk resulting in higher risk levels. Based on the tasks performed and locations of different devices, and according to the role and impact of the devices in the thermal power generation system, the criticality levels of different devices are classified, with higher role and impact resulting in higher criticality levels. The final health index calculation formula is as follows: ; Where H represents the health index, R represents the risk level, and K represents the critical level.

[0010] Preferably, a data visualization interface is designed to present the health index of different devices in the form of charts, showing the changing trends of the health index of different devices, so as to facilitate quick understanding of the device status.

[0011] Preferably, in S6, priority is given to predicting the failure development trend of equipment with a higher health index. Based on the failure development trend prediction results, priority maintenance plans are formulated for equipment with a less optimistic failure development trend. Emergency repairs are carried out when necessary, and the frequency of maintenance is increased.

[0012] Preferably, targeted maintenance plans are developed for critical and high-risk equipment, and maintenance schedules are determined based on the equipment's operating hours, with maintenance carried out at the designated times.

[0013] Therefore, the health management method for thermal power generation equipment described above can promptly inspect and maintain key and high-risk equipment, reduce the failure rate of key equipment, ensure the normal operation of the thermal power generation system, prioritize equipment according to importance, ensure that key equipment and equipment with high risk indicators are maintained first, and maintain other equipment at appropriate times, effectively realizing the rational arrangement of maintenance sequence and method, avoiding the waste of resources caused by over-maintenance, and balancing maintenance costs.

[0014] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0015] Figure 1 This is a flowchart of an embodiment of a health management method for thermal power generation equipment according to the present invention; Figure 2 This is a trend chart of the health index of the equipment in an embodiment of the health management method for thermal power generation equipment according to the present invention. Detailed Implementation

[0016] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0017] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0018] Example 1 like Figure 1 As shown, the present invention provides a health management method for thermal power generation equipment, comprising the following steps: S1. Periodically collect historical operating data of the equipment.

[0019] Temperature, vibration, pressure, and current data are collected from each device. These operational data are collected periodically and will be used directly for further data analysis and processing.

[0020] S2. The collected historical operational data is preprocessed, including cleaning and extracting feature information. The preprocessed data is then classified into healthy datasets and fault datasets, which serve as historical data samples.

[0021] S3. Construct a neural network fault prediction model and train and test the neural network fault prediction model based on historical data samples.

[0022] Both the health dataset and the fault dataset were divided into training and test sets, respectively, and a neural network fault prediction model was constructed. The training set was imported into the neural network fault prediction model for training, which established the feature layers of the model, and the training results were output. The test set was then imported into the neural network fault prediction model for testing, and the test results were output. The accuracy of the neural network fault prediction model's output was verified by comparing the test results with the training results.

[0023] S4. Use the trained neural network fault prediction model to collect equipment operation data in real time for fault prediction.

[0024] S5. Based on the prediction results of historical operation data, classify the risk level of the operation status of different equipment, classify the critical level according to the importance of different equipment, and combine the failure risk level and critical level of the same equipment to obtain the health index of the equipment. Based on the health index, prioritize the maintenance of critical equipment with high risk level.

[0025] The neural network fault prediction model categorizes fault prediction results for different equipment, with higher fault risk resulting in a higher risk level. Based on the tasks performed and locations of different equipment within the thermal power generation system, the criticality level of each piece of equipment is further classified, with greater role and impact resulting in a higher criticality level. The final health index is calculated using the following formula: Where H represents the health index, R represents the risk level, and K represents the critical level.

[0026] Design a data visualization interface to intuitively present the health indices of different devices in the form of charts, showing the changing trends of these indices and facilitating a quick understanding of the device's status. Figure 2 As shown.

[0027] S6. For the health index of each piece of equipment, use multiple regression analysis to predict the failure development trend of the equipment and arrange maintenance in the early stage of failure.

[0028] Prioritize the prediction of failure development trends for equipment with higher health indices. Based on the prediction results, formulate priority maintenance plans for equipment with less optimistic failure development trends, carry out emergency repairs when necessary, and increase the frequency of maintenance.

[0029] Develop targeted maintenance plans for critical and high-risk equipment, determine maintenance schedules based on equipment operating hours, and carry out maintenance at the designated schedules.

[0030] Therefore, the health management method for thermal power generation equipment described above can promptly inspect and maintain key and high-risk equipment, reduce the failure rate of key equipment, ensure the normal operation of the thermal power generation system, prioritize equipment according to importance, ensure that key equipment and equipment with high risk indicators are maintained first, and maintain other equipment at appropriate times, effectively realizing the rational arrangement of maintenance sequence and method, avoiding the waste of resources caused by over-maintenance, and balancing maintenance costs.

[0031] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A health management method for thermal power generation equipment, characterized in that, Includes the following steps: S1. Periodically collect historical operating data of the equipment; S2. Preprocess the collected historical operational data and classify the preprocessed data into health datasets and fault datasets, and use them as historical data samples. S3. Construct a neural network fault prediction model and train and test the neural network fault prediction model based on historical data samples. S4. Use the trained neural network fault prediction model to collect equipment operating data in real time for fault prediction. S5. Based on the prediction results of historical operation data, the risk level of the operation status of different equipment is divided into risk levels, and the critical level is divided according to the importance of different equipment. The failure risk level and critical level of the same equipment are integrated and calculated to obtain the health index of the equipment. Based on the health index, the critical equipment with high risk level is prioritized for maintenance. S6. For the health index of each piece of equipment, use multiple regression analysis to predict the failure development trend of the equipment and arrange maintenance in the early stage of failure.

2. The health management method for thermal power generation equipment according to claim 1, characterized in that: In S2, preprocessing involves cleaning historical operational data and extracting feature information from it.

3. The health management method for thermal power generation equipment according to claim 1, characterized in that: In S3, the health dataset and the fault dataset are divided into training set and test set respectively, and a neural network fault prediction model is constructed. The training set is imported into the neural network fault prediction model for training to establish the feature layer of the neural network fault prediction model and output the training results. The test set is then imported into the neural network fault prediction model for testing and the test results are output. The accuracy of the output results of the neural network fault prediction model is verified by comparing the test results with the training results.

4. The health management method for thermal power generation equipment according to claim 1, characterized in that: In S5, the neural network fault prediction model categorizes the fault prediction results for different devices, with higher fault risks resulting in higher risk levels. Based on the tasks performed and locations of different devices, and according to their role and impact within the thermal power generation system, the criticality level of different devices is further categorized, with higher roles and impacts resulting in higher criticality levels. The final health index calculation formula is as follows: ； Where H represents the health index, R represents the risk level, and K represents the critical level.

5. A health management method for thermal power generation equipment according to claim 4, characterized in that: Design a data visualization interface to intuitively present the health index of different devices in the form of charts, showing the changing trends of the health index of different devices, so as to quickly understand the status of the devices.

6. The health management method for thermal power generation equipment according to claim 1, characterized in that: In S6, priority is given to predicting the failure development trend of equipment with a higher health index. Based on the failure development trend prediction results, priority maintenance plans are formulated for equipment with a less optimistic failure development trend. Emergency repairs are carried out when necessary, and the frequency of maintenance is increased.

7. A health management method for thermal power generation equipment according to claim 6, characterized in that: Develop targeted maintenance plans for critical and high-risk equipment, determine maintenance schedules based on equipment operating hours, and carry out maintenance at the designated schedules.

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