Thermal power plant equipment operation optimization technology based on intelligent diagnosis

Abstract

The invention discloses a thermal power plant equipment operation optimization technology based on intelligent diagnosis, relates to the technical field of thermal power plant equipment operation, maintenance and optimization technologies, and aims to solve the problems that when a current intelligent operation and maintenance system applied to thermal power plant equipment processes complex data, an effective feature extraction step is often lacked, and the operation efficiency is high. The method comprises the following steps: S1, a real-time monitoring and data acquisition process: deploying a high-precision sensor on key equipment of the thermal power plant, acquiring equipment operation data in real time, and transmitting the data to a data processing center in real time in combination with a data acquisition system supporting remote communication; according to the method, an advanced feature extraction technology is introduced, and an autonomous learning mechanism is combined, so that the system can continuously learn and optimize own performance, and powerful support is provided for intelligent operation, maintenance and optimization of thermal power plant equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal power plant equipment operation, maintenance and optimization technology, and more specifically, to a thermal power plant equipment operation optimization technology based on intelligent diagnosis. Background Art

[0002] Traditional thermal power plant equipment operation and maintenance relies on manual inspections and regular maintenance, which has problems such as delayed response, high maintenance costs, and limited prediction accuracy. With the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, intelligent operation and maintenance has become an important way to improve the efficiency and economic benefits of thermal power plant operation and maintenance.

[0003] However, the existing intelligent operation and maintenance systems often lack effective feature extraction steps when processing complex data, which limits the accuracy and efficiency of subsequent intelligent diagnosis. In view of this, we propose a thermal power plant equipment operation optimization technology based on intelligent diagnosis. Summary of the invention

[0004] The purpose of the present invention is to provide a thermal power plant equipment operation optimization technology based on intelligent diagnosis, so as to solve the technical problem that the current intelligent operation and maintenance system applied to thermal power plant equipment often lacks an effective feature extraction step when processing complex data, resulting in limited accuracy and efficiency of subsequent intelligent diagnosis.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: A thermal power plant equipment operation optimization technology based on intelligent diagnosis, comprising the following steps:

[0006] S1, real-time monitoring and data collection process;

[0007] Deploy high-precision sensors on key equipment in thermal power plants to collect equipment operation data in real time, and combine with a data acquisition system that supports remote communications to transmit the data to a data processing center in real time;

[0008] S2, data processing and feature extraction process;

[0009] S201, data preprocessing: performing preprocessing operations on the collected raw data to obtain data A, and then extracting features from data A;

[0010] S3, intelligent diagnosis process;

[0011] Establish an equipment health status assessment model, and then input the processed data A and its features into the model for intelligent analysis to identify equipment abnormal status and potential failures, and predict failures;

[0012] S4, autonomous learning mechanism process;

[0013] Use the newly collected data A and the diagnosis results to continuously train and optimize the diagnosis model, and constantly modify the model parameters. Combined with the analysis of historical diagnosis cases and maintenance records, valuable information can be extracted to guide the formulation of future optimization strategies.

[0014] S5. Optimize the strategy generation process;

[0015] Based on the results of intelligent diagnosis and analysis of the autonomous learning module, the system comprehensively considers the mutual influence and synergy between equipment, takes into account the overall operating efficiency and economy of the thermal power plant, and generates personalized equipment maintenance optimization strategies;

[0016] S6. Execution feedback and continuous optimization process;

[0017] The optimization strategy will be issued to relevant departments and personnel for execution, and the implementation process will be tracked. The implementation effect will be compared and analyzed with the expected goals, and a closed-loop optimization mechanism will be formed based on the analysis.

[0018] The present invention effectively improves the accuracy and efficiency of data processing and intelligent diagnosis by introducing advanced feature extraction technology. At the same time, combined with the autonomous learning mechanism, the system can continuously learn and optimize its own performance, providing strong support for the intelligent operation and optimization of thermal power plant equipment.

[0019] Preferably, the equipment operation data includes one or more of temperature, vibration, pressure and flow.

[0020] Preferably, the S2 data processing and feature extraction process further includes the following steps:

[0021] S201, data preprocessing: performing preprocessing operations on the collected raw data to improve data quality and obtain data A;

[0022] S202, feature extraction step:

[0023] The extraction of features in data A is achieved through time domain feature extraction, frequency domain feature extraction, time-frequency domain joint feature extraction and advanced feature construction.

[0024] Preferably, the preprocessing operation includes one or more of cleaning, denoising and filtering.

[0025] Preferably, the feature extraction step S202 further includes the following steps:

[0026] S2021. Time domain feature extraction: Calculate the mean, variance, peak and other statistics of the data to reflect the basic status of the equipment operation;

[0027] S2022. Frequency domain feature extraction: Use the Fourier transform method to convert the time domain signal into the frequency domain and extract spectrum features, such as main frequency, spectrum energy distribution, etc., to identify the vibration characteristics of the equipment;

[0028] S2023. Joint feature extraction in time and frequency domains: Using wavelet transform and Hilbert-Huang transform methods, combined with time domain and frequency domain information, more refined time and frequency features are extracted to capture the dynamic changes of equipment operation;

[0029] S2024, advanced feature construction: Based on the above basic features, use principal component analysis and autoencoder technology to construct advanced features to further reduce data dimensions and improve model processing efficiency and generalization capabilities.

[0030] Preferably, the S3 intelligent diagnosis process also includes the following steps:

[0031] S301, model generation;

[0032] Use machine learning algorithms to establish equipment health status assessment models;

[0033] S302, abnormality and potential fault identification;

[0034] The processed data A and its features are input into the model for intelligent analysis to identify abnormal equipment status and potential failures;

[0035] S303, fault prediction;

[0036] Determine the type, analyze the cause and predict the development trend of the identified faults.

[0037] Preferably, the machine learning algorithm includes one or more of deep learning and support vector machine.

[0038] Preferably, the S4 autonomous learning mechanism process also includes the following steps:

[0039] S401, introduce self-learning module;

[0040] Continuously train and optimize diagnostic models using newly acquired data and diagnostic results;

[0041] S402, model parameter correction;

[0042] Through the feedback loop, the model parameters are continuously corrected to improve the diagnostic accuracy and generalization ability;

[0043] S403, information analysis and extraction;

[0044] The self-learning module can also analyze historical diagnostic cases and maintenance records to extract valuable information to guide the formulation of future optimization strategies.

[0045] Preferably, the optimization strategy includes one or more of adjusting operating parameters, formulating a preventive maintenance plan, and optimizing maintenance procedures.

[0046] Preferably, the S6 execution feedback and continuous optimization process further includes the following steps:

[0047] S601, strategy distribution;

[0048] Distribute optimization strategies to relevant departments and personnel for execution;

[0049] S602, tracking feedback;

[0050] Track the execution process and collect actual operation data and user feedback;

[0051] S602, closed loop mechanism optimization;

[0052] Compare and analyze the execution effect with the expected goal, adjust the optimization strategy according to the analysis results, and form a closed-loop optimization mechanism.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The invention is based on the thermal power plant equipment operation optimization method of intelligent diagnosis, autonomous learning and efficient feature extraction steps. By introducing advanced feature extraction technology, the accuracy and efficiency of data processing and intelligent diagnosis are effectively improved. At the same time, combined with the autonomous learning mechanism, the system can continuously learn and optimize its own performance, providing strong support for the intelligent operation and optimization of thermal power plant equipment. DETAILED DESCRIPTION

[0055] The present invention relates to a thermal power plant equipment operation optimization technology based on intelligent diagnosis, comprising the following steps:

[0056] S1, real-time monitoring and data collection process;

[0057] Deploy high-precision sensors on key equipment in thermal power plants to collect equipment operation data in real time, and combine with a data acquisition system that supports remote communication to transmit the data in real time to a data processing center. The equipment operation data includes one or more of temperature, vibration, pressure, and flow rate;

[0058] S2, data processing and feature extraction process;

[0059] S201, data preprocessing: performing preprocessing operations on the collected raw data to obtain data A, and then extracting features in the data A. The preprocessing operations include one or more of cleaning, denoising, and filtering;

[0060] S3, intelligent diagnosis process;

[0061] Establish an equipment health status assessment model, and then input the processed data A and its features into the model for intelligent analysis to identify equipment abnormal status and potential failures, and predict failures;

[0062] S4, autonomous learning mechanism process;

[0063] Use the newly collected data A and the diagnosis results to continuously train and optimize the diagnosis model, and constantly modify the model parameters. Combined with the analysis of historical diagnosis cases and maintenance records, valuable information can be extracted to guide the formulation of future optimization strategies.

[0064] S5. Optimize the strategy generation process;

[0065] Based on the results of intelligent diagnosis and analysis of the autonomous learning module, the system comprehensively considers the mutual influence and synergy between equipment, takes into account the overall operating efficiency and economy of the thermal power plant, and generates personalized equipment maintenance optimization strategies;

[0066] S6. Execution feedback and continuous optimization process;

[0067] The optimization strategy will be issued to relevant departments and personnel for execution, and the implementation process will be tracked. The implementation effect will be compared and analyzed with the expected goals, and a closed-loop optimization mechanism will be formed based on the analysis.

[0068] The present invention effectively improves the accuracy and efficiency of data processing and intelligent diagnosis by introducing advanced feature extraction technology. At the same time, combined with the autonomous learning mechanism, the system can continuously learn and optimize its own performance, providing strong support for the intelligent operation and optimization of thermal power plant equipment.

[0069] In an embodiment of the present invention, the S2 data processing and feature extraction process further includes the following steps:

[0070] S201, data preprocessing: performing preprocessing operations on the collected raw data to improve data quality and obtain data A;

[0071] S202, feature extraction step:

[0072] The extraction of features in data A is achieved through time domain feature extraction, frequency domain feature extraction, time-frequency domain joint feature extraction and advanced feature construction.

[0073] In an embodiment of the present invention, the feature extraction step S202 further includes the following steps:

[0074] S2021. Time domain feature extraction: Calculate the mean, variance, peak and other statistics of the data to reflect the basic status of the equipment operation;

[0075] S2022. Frequency domain feature extraction: Use the Fourier transform method to convert the time domain signal into the frequency domain and extract spectrum features, such as main frequency, spectrum energy distribution, etc., to identify the vibration characteristics of the equipment;

[0076] S2023. Joint feature extraction in time and frequency domains: Using wavelet transform and Hilbert-Huang transform methods, combined with time domain and frequency domain information, more refined time and frequency features are extracted to capture the dynamic changes of equipment operation;

[0077] S2024, advanced feature construction: Based on the above basic features, use principal component analysis and autoencoder technology to construct advanced features to further reduce data dimensions and improve model processing efficiency and generalization capabilities.

[0078] In an embodiment of the present invention, the S3 intelligent diagnosis process further includes the following steps:

[0079] S301, model generation;

[0080] Establishing a device health status assessment model using a machine learning algorithm, wherein the machine learning algorithm includes one or more of deep learning and support vector machine;

[0081] S302, abnormality and potential fault identification;

[0082] The processed data A and its features are input into the model for intelligent analysis to identify abnormal equipment status and potential failures;

[0083] S303, fault prediction;

[0084] Determine the type, analyze the cause and predict the development trend of the identified faults.

[0085] In an embodiment of the present invention, the S4 autonomous learning mechanism process further includes the following steps:

[0086] S401, introduce self-learning module;

[0087] Continuously train and optimize diagnostic models using newly acquired data and diagnostic results;

[0088] S402, model parameter correction;

[0089] Through the feedback loop, the model parameters are continuously corrected to improve the diagnostic accuracy and generalization ability;

[0090] S403, information analysis and extraction;

[0091] The self-learning module can also analyze historical diagnostic cases and maintenance records to extract valuable information to guide the formulation of future optimization strategies, including one or more of adjusting operating parameters, formulating preventive maintenance plans, and optimizing maintenance processes.

[0092] In an embodiment of the present invention, the S6 execution feedback and continuous optimization process further includes the following steps:

[0093] S601, strategy distribution;

[0094] Distribute optimization strategies to relevant departments and personnel for execution;

[0095] S602, tracking feedback;

[0096] Track the execution process and collect actual operation data and user feedback;

[0097] S602, closed loop mechanism optimization;

[0098] Compare and analyze the execution effect with the expected goal, adjust the optimization strategy according to the analysis results, and form a closed-loop optimization mechanism.

[0099] The embodiments of the present invention disclose preferred embodiments, but are not limited thereto. A person skilled in the art can easily understand the spirit of the present invention based on the above embodiments and make different extensions and changes. However, as long as they do not deviate from the spirit of the present invention, they are all within the protection scope of the present invention.

Claims

1. A thermal power plant equipment operation optimization technology based on intelligent diagnosis, characterized in that: The following steps are involved: S1, real-time monitoring and data collection process; Deploy high-precision sensors on key equipment in thermal power plants to collect equipment operation data in real time, and combine with a data acquisition system that supports remote communications to transmit the data to a data processing center in real time; S2, data processing and feature extraction process; S201, data preprocessing: performing preprocessing operations on the collected raw data to obtain data A, and then extracting features from data A; S3, intelligent diagnosis process; Establish an equipment health status assessment model, and then input the processed data A and its features into the model for intelligent analysis to identify equipment abnormal status and potential failures, and predict failures; S4, autonomous learning mechanism process; Use the newly collected data A and the diagnosis results to continuously train and optimize the diagnosis model, and constantly modify the model parameters. Combined with the analysis of historical diagnosis cases and maintenance records, valuable information can be extracted to guide the formulation of future optimization strategies. S5. Optimize the strategy generation process; Based on the results of intelligent diagnosis and analysis of the autonomous learning module, the system comprehensively considers the mutual influence and synergy between equipment, takes into account the overall operating efficiency and economy of the thermal power plant, and generates personalized equipment maintenance optimization strategies; S6. Execution feedback and continuous optimization process; The optimization strategy will be issued to relevant departments and personnel for execution, and the implementation process will be tracked. The implementation effect will be compared and analyzed with the expected goals, and a closed-loop optimization mechanism will be formed based on the analysis.

2. According to claim 1, a thermal power plant equipment operation optimization technology based on intelligent diagnosis is characterized in that: The equipment operation data includes one or more of temperature, vibration, pressure, and flow rate.

3. The thermal power plant equipment operation optimization technology based on intelligent diagnosis according to claim 1 is characterized in that: The S2 data processing and feature extraction process also includes the following steps: S201, data preprocessing: performing preprocessing operations on the collected raw data to improve data quality and obtain data A; S202, feature extraction step: The extraction of features in data A is achieved through time domain feature extraction, frequency domain feature extraction, time-frequency domain joint feature extraction and advanced feature construction.

4. The thermal power plant equipment operation optimization technology based on intelligent diagnosis according to claim 3 is characterized in that: The preprocessing operation includes one or more of cleaning, denoising, and filtering.

5. The thermal power plant equipment operation optimization technology based on intelligent diagnosis according to claim 3 is characterized in that: The step S202 of feature extraction also includes the following steps: S2021. Time domain feature extraction: Calculate the mean, variance, peak and other statistical quantities of the data to reflect the basic status of the equipment operation; S2022. Frequency domain feature extraction: Use the Fourier transform method to convert the time domain signal into the frequency domain and extract spectrum features, such as main frequency, spectrum energy distribution, etc., to identify the vibration characteristics of the equipment; S2023. Joint feature extraction in time and frequency domains: Using wavelet transform and Hilbert-Huang transform methods, combined with time domain and frequency domain information, more refined time and frequency features are extracted to capture the dynamic changes of equipment operation; S2024, advanced feature construction: Based on the above basic features, use principal component analysis and autoencoder technology to construct advanced features to further reduce data dimensions and improve model processing efficiency and generalization capabilities.

6. The thermal power plant equipment operation optimization technology based on intelligent diagnosis according to claim 1 is characterized in that: The S3 intelligent diagnosis process also includes the following steps: S301, model generation; Use machine learning algorithms to establish equipment health status assessment models; S302, abnormality and potential fault identification; The processed data A and its features are input into the model for intelligent analysis to identify abnormal equipment status and potential failures; S303, fault prediction; Determine the type, analyze the cause and predict the development trend of the identified faults.

7. The thermal power plant equipment operation optimization technology based on intelligent diagnosis according to claim 6 is characterized in that: The machine learning algorithm includes one or more of deep learning and support vector machine.

8. The thermal power plant equipment operation optimization technology based on intelligent diagnosis according to claim 1 is characterized in that: The S4 autonomous learning mechanism process also includes the following steps: S401, introduce self-learning module; Continuously train and optimize diagnostic models using newly acquired data and diagnostic results; S402, model parameter correction; Through the feedback loop, the model parameters are continuously corrected to improve the diagnostic accuracy and generalization ability; S403, information analysis and extraction; The self-learning module can also analyze historical diagnostic cases and maintenance records to extract valuable information to guide the formulation of future optimization strategies.

9. The thermal power plant equipment operation optimization technology based on intelligent diagnosis according to claim 1 is characterized in that: The optimization strategy includes one or more of adjusting operating parameters, formulating a preventive maintenance plan, and optimizing the maintenance process.

10. The thermal power plant equipment operation optimization technology based on intelligent diagnosis according to claim 1 is characterized by: The S6 execution feedback and continuous optimization process also includes the following steps: S601, strategy distribution; Distribute optimization strategies to relevant departments and personnel for execution; S602, tracking feedback; Track the execution process and collect actual operation data and user feedback; S602, closed loop mechanism optimization; Compare and analyze the execution effect with the expected goal, adjust the optimization strategy according to the analysis results, and form a closed-loop optimization mechanism.