Electric power plant preventive maintenance monitoring system based on digital twinborn and equipment state prediction

Abstract

The invention discloses an electric power plant preventive maintenance monitoring system based on digital twinning and equipment state prediction, and relates to the field of prediction analysis, and the system comprises a real-time data collection module which is used for periodically obtaining the real-time operation parameters of each operation part of electric equipment in the current state through a sensor; the deviation calculation module is used for comparing the real-time operation parameter with a standard operation parameter of a corresponding operation part, obtaining a deviation value between the real-time operation data of the operation part in each state and the standard operation parameter by calculating a difference value, and generating a deviation value sequence; the system can accurately predict the probability of a specific deterioration event occurring in the future before a part has a substantive fault, so that maintenance personnel can obtain early warning when the equipment health condition is critical to a safety threshold value, thereby having sufficient time to plan and execute targeted preventive maintenance measures, and improving the maintenance efficiency. And the impact of sudden shutdown on the stability of the power grid and the economic benefit of a factory is avoided.

Description

Technical Field

[0001] This invention relates to the field of predictive analytics, specifically to a preventive maintenance monitoring system for power plant areas based on digital twins and equipment status prediction. Background Technology

[0002] Electricity is the lifeblood of the national economy, and any unplanned outage can cause enormous economic losses and social impacts. Therefore, power companies have placed unprecedented demands on equipment reliability and predictive maintenance. Modern power plants are characterized by highly complex and interconnected equipment, and are equipped with a large number of sensors, generating massive amounts of operational data. This provides a data foundation for refined condition monitoring and forecasting, but also poses significant challenges to data processing and analysis capabilities.

[0003] Traditional reactive maintenance intervenes only after equipment failure, resulting in long unplanned downtimes, high repair costs, and the potential for secondary damage or safety incidents. Regular maintenance, based on fixed times or operating cycles without considering the actual health status of the equipment, may lead to unnecessary maintenance when equipment is in good condition or unexpected equipment damage during maintenance intervals. Many existing monitoring systems only provide simple threshold alarms, failing to detect slow, gradual performance degradation. Furthermore, thresholds are often set based on experience, unable to adapt to equipment aging or environmental changes, and fail to extract deeper equipment health information and fault evolution patterns from the data. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a preventive maintenance monitoring system for power plant areas based on digital twins and equipment status prediction, which can effectively solve the problems of the existing technology.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] This invention discloses a preventive maintenance monitoring system for power plant areas based on digital twins and equipment status prediction, comprising:

[0009] The real-time data acquisition module is used to periodically acquire real-time operating parameters of the current status of each operating component of the electrical equipment through sensors;

[0010] The standard parameter storage module is used to store the preset standard operating parameters of each operating component of the electrical equipment under normal operating conditions and their abnormal alarm thresholds.

[0011] The deviation calculation module is used to compare the real-time operating parameters with the standard operating parameters of the corresponding operating components, calculate the difference, obtain the deviation value between the real-time operating data of the operating component in each state and the standard operating parameters, and generate a deviation value sequence.

[0012] The trend recognition module is used to identify the trend of the deviation curve of the device within the current period, including the trend direction, rate of change and periodic characteristics, based on the deviation value sequence and using time series analysis, and outputs trend indicators;

[0013] The model building module is used to build state prediction models using strong learning algorithms, and trains the models with historical deviation data and deterioration event labels.

[0014] The event prediction module is used to extract the state prediction model trained by the model building module. The deviation curve trend of each operating component of the current equipment is input into the state prediction model to predict the probability of one or more deterioration events corresponding to each component within a specified time period in the future. Deterioration events include component wear, overheating, insulation aging or mechanical failure.

[0015] The threshold comparison module is used to compare the probability of the occurrence of the deterioration event with a preset safety threshold, which is set based on the critical state of the equipment or the cost-effectiveness of maintenance, and to identify deterioration events that exceed the safety threshold.

[0016] The measure matching module is used to match related maintenance measures, including component replacement, lubrication, parameter adjustment, or shutdown for maintenance, from the maintenance knowledge base based on the probability of deterioration events exceeding the safety threshold, and generate maintenance suggestion plans.

[0017] Furthermore, the real-time operating parameters in the real-time data acquisition module include at least one or more of temperature, vibration, current, voltage, and power factor, and the real-time operating parameters are transmitted to the management terminal. The acquired real-time operating parameters are filtered, denoised, or normalized to eliminate environmental interference and measurement errors and improve data quality.

[0018] Furthermore, the standard parameter storage module supports a dynamic update mechanism, periodically extracting new standard parameters and abnormal alarm thresholds from historical operating data, verifying their effectiveness, and then updating them.

[0019] Furthermore, the process by which the trend recognition module identifies the trend of the deviation curve is as follows:

[0020] Receive the deviation value sequence and construct a discrete time series dataset at fixed time intervals; perform a stationarity test on the dataset, and if the sequence is non-stationary, convert it into a stationary time series through difference operations or logarithmic transformation methods;

[0021] It is used to fit stationary time series using an autoregressive integral moving average model, capture the autocorrelation, trend and periodic components in the series, and construct a time series model;

[0022] Based on the fitted time series model, the predicted deviation values ​​for one or more future time points are calculated and connected to form a predicted trajectory. By performing first derivative analysis on the predicted trajectory, the direction of the deviation curve trend is quantified as rising, falling, or stable. The strength and rate of change of the trend are quantified by calculating the slope of the linear regression or the rate of change of the moving average.

[0023] Furthermore, the trend recognition module is interconnected with a judgment module via a wireless network. The judgment module is used to compare the deviation value obtained by the trend recognition module with the abnormal alarm threshold preset by the standard parameter storage module in real time. The abnormal alarm threshold is dynamically adjusted based on the equipment safety standard or historical fault data, and an alarm signal is triggered when the deviation value exceeds the abnormal alarm threshold.

[0024] Furthermore, the process of constructing the state prediction model in the model construction module includes:

[0025] The deviation value sequence of each operating component of the electrical equipment and the corresponding deterioration event label data are collected from the historical database. The historical database is constructed from long-term operation monitoring data and covers a variety of operating conditions throughout the entire life cycle of the equipment.

[0026] Strong learning algorithm was selected as the basic framework of the model. The selected algorithm was initialized by setting the number of network layers, number of nodes and activation function type. Iterative training was carried out through forward and backward propagation mechanisms, using historical deviation value sequence as input features and deterioration event labels as supervision targets to minimize prediction error.

[0027] The model performance is evaluated using a validation dataset. By calculating accuracy, recall, and confusion matrix metrics, overfitting or underfitting issues are identified, and regularization techniques or early stopping methods are used to optimize the model's generalization ability.

[0028] Furthermore, the formula for calculating the probability of a certain deteriorating event in the event prediction module is as follows:

[0029]

[0030] In the formula, P(E|D) represents the probability of the deterioration event E occurring given the current deviation curve trend data D, exp represents the natural exponential function, w represents the weight vector, X represents the input feature vector, and b represents the bias term.

[0031] Furthermore, the measure matching module submits the maintenance suggestion plan to the management end through an API interface or message queue, and supports the allocation and tracking of maintenance tasks.

[0032] Furthermore, the judgment module is interconnected with an alarm execution module via a wireless network. The alarm execution module is used to respond to the alarm signal and immediately alarm via an audible and visual alarm, SMS notification, or management platform pop-up, and record the alarm time, device, and component information.

[0033] Furthermore, the deviation calculation module is interconnected with the real-time data acquisition module, the standard parameter storage module, and the trend recognition module via a wireless network; the model building module is interconnected with the trend recognition module and the event prediction module via a wireless network; and the threshold comparison module is interconnected with the event prediction module and the measure matching module via a wireless network.

[0034] (III) Beneficial Effects

[0035] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects:

[0036] 1. By using digital twin technology, the system continuously compares the micro-deviations between real-time data and standard conditions. By analyzing the trend of the deviation curve, it identifies the degradation process of equipment performance. Combined with a predictive model built using strong learning algorithms, the system can accurately predict the probability of specific deterioration events occurring in a component before a substantial failure occurs. This allows maintenance personnel to receive early warnings when the equipment's health condition is close to a safety threshold, giving them sufficient time to plan and implement targeted preventative maintenance measures. This avoids the impact of sudden shutdowns on grid stability and plant economic benefits, ensuring the continuity and reliability of power supply.

[0037] 2. Based on probabilistic events exceeding safety thresholds, the system can automatically match and recommend optimal maintenance measures, directly submitting them to the management end. This significantly improves decision-making efficiency and accuracy, reduces misjudgments caused by differences in personnel experience, and continuously collects the actual effects of maintenance measures. This data is then fed back into the predictive model and standard parameter library, allowing them to continuously evolve and become more accurate with equipment aging and environmental changes. This not only optimizes the allocation of maintenance manpower and spare parts resources, avoiding unnecessary maintenance waste, but also forms a full lifecycle health record for the equipment from commissioning to decommissioning. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0039] Figure 1 This is a schematic diagram of the framework of the present invention.

[0040] The labels in the diagram represent: 1. Real-time data acquisition module; 2. Standard parameter storage module; 3. Deviation calculation module; 4. Trend recognition module; 5. Model building module; 6. Event prediction module; 7. Threshold comparison module; 8. Measure matching module; 9. Judgment module; 10. Alarm execution module. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0042] The present invention will be further described below with reference to embodiments.

[0043] This embodiment presents a preventative maintenance monitoring system for power plant areas based on digital twins and equipment status prediction, such as... Figure 1 As shown, it includes:

[0044] The real-time data acquisition module 1 is used to periodically acquire real-time operating parameters of the current status of each operating component of the electrical equipment through sensors. The real-time operating parameters include at least one or more of temperature, vibration, current, voltage, and power factor. The real-time operating parameters are transmitted to the management terminal, and the acquired real-time operating parameters are filtered, denoised, or normalized to eliminate environmental interference and measurement errors and improve data quality.

[0045] The standard parameter storage module 2 is used to store the preset standard operating parameters and abnormal alarm thresholds of each operating component of the electrical equipment under normal operating conditions. The standard parameter storage module 2 supports a dynamic update mechanism, which periodically extracts new standard parameters and abnormal alarm thresholds from historical operating data, verifies their effectiveness, and then updates them.

[0046] Deviation calculation module 3 is used to compare real-time operating parameters with standard operating parameters of the corresponding operating component, calculate the difference, obtain the deviation value between the real-time operating data of the operating component in each state and the standard operating parameters, and generate a deviation value sequence.

[0047] Trend identification module 4 is used to identify the deviation curve trend of the device within the current period, including trend direction, rate of change, and periodic characteristics, based on the deviation value sequence and using time series analysis, and outputs trend indicators; the process of identifying the deviation curve trend is as follows:

[0048] Receive the deviation value sequence and construct a discrete time series dataset at fixed time intervals; perform a stationarity test on the dataset, and if the sequence is non-stationary, convert it into a stationary time series through difference operations or logarithmic transformation methods;

[0049] It is used to fit stationary time series using an autoregressive integral moving average model, capture the autocorrelation, trend and periodic components in the series, and construct a time series model;

[0050] Based on the fitted time series model, the predicted deviation values ​​for one or more future time points are calculated and connected to form a predicted trajectory. By performing first derivative analysis on the predicted trajectory, the direction of the deviation curve trend is quantified as rising, falling, or stable. The strength and rate of change of the trend are quantified by calculating the slope of the linear regression or the rate of change of the moving average.

[0051] The trend recognition module 4 is connected to the judgment module 9 via a wireless network. The judgment module 9 is used to compare the deviation value obtained by the trend recognition module 4 with the abnormal alarm threshold preset by the standard parameter storage module 2 in real time. The abnormal alarm threshold is dynamically adjusted based on the equipment safety standard or historical fault data, and an alarm signal is triggered when the deviation value exceeds the abnormal alarm threshold.

[0052] The judgment module 9 is connected to the alarm execution module 10 via a wireless network. The alarm execution module 10 is used to respond to alarm signals and immediately alarm via an audible and visual alarm, SMS notification or management platform pop-up, and record the alarm time, equipment and component information.

[0053] Model building module 5 is used to build a state prediction model using a strong learning algorithm, training the model with historical deviation data and deterioration event labels; the process of building the state prediction model includes:

[0054] The deviation value sequence of each operating component of the electrical equipment and the corresponding deterioration event label data are collected from the historical database. The historical database is built from long-term operation monitoring data and covers a variety of operating conditions throughout the entire life cycle of the equipment.

[0055] Strong learning algorithm was selected as the basic framework of the model. The selected algorithm was initialized by setting the number of network layers, number of nodes and activation function type. Iterative training was carried out through forward and backward propagation mechanisms, using historical deviation value sequence as input features and deterioration event labels as supervision targets to minimize prediction error.

[0056] The model performance is evaluated using a validation dataset. By calculating accuracy, recall, and confusion matrix metrics, overfitting or underfitting issues are identified, and regularization techniques or early stopping methods are used to optimize the model's generalization ability.

[0057] Event prediction module 6 is used to extract the state prediction model trained by model building module 5. The deviation curve trend of each operating component of the current equipment is input into the state prediction model to predict the probability of one or more deterioration events corresponding to each component within a specified time period in the future. Deterioration events include component wear, overheating, insulation aging or mechanical failure.

[0058] The threshold comparison module 7 is used to compare the probability of a deterioration event with a preset safety threshold, which is set based on the critical state of the equipment or the cost-effectiveness of maintenance, and to identify deterioration events that exceed the safety threshold.

[0059] The measure matching module 8 is used to match related maintenance measures, including component replacement, lubrication, parameter adjustment, or shutdown for maintenance, from the maintenance knowledge base based on the probability of deterioration events exceeding the safety threshold, and generate maintenance suggestion plans. The measure matching module 8 submits the maintenance suggestion plans to the management end through API interface or message queue, and supports the allocation and tracking of maintenance tasks.

[0060] Deviation calculation module 3 is interconnected with real-time data acquisition module 1, standard parameter storage module 2 and trend recognition module 4 via wireless network. Model building module 5 is interconnected with trend recognition module 4 and event prediction module 6 via wireless network. Threshold comparison module 7 is interconnected with event prediction module 6 and measure matching module 8 via wireless network.

[0061] Compared with existing technologies, by integrating real-time sensor data acquisition, dynamic threshold updates, time series analysis, and strong learning algorithms, the system achieves accurate prediction of the operating status of electrical equipment and intelligent maintenance decisions. The system can identify component deterioration trends in advance and automatically generate customized maintenance plans, thereby effectively avoiding sudden failures and reducing downtime. At the same time, by dynamically adjusting alarm thresholds and model parameters, the system improves adaptability and maintenance efficiency, reduces operating costs, and overcomes the lag and inaccuracy of traditional periodic maintenance or simple threshold alarms.

[0062] At other levels, in this embodiment, the formula for calculating the probability of a certain deteriorating event is:

[0063]

[0064] In the formula, P(E|D) represents the probability of a deterioration event E occurring given the current deviation curve trend data D. Deterioration events E include component wear, overheating, or mechanical failure types. The probability value ranges from 0 to 1, with higher values ​​indicating a greater likelihood of the event occurring. exp represents the natural exponential function, w represents the weight vector, which is learned through the model training process. The weight vector reflects the degree of influence of different deviation characteristics on deterioration events, and its dimension matches the input features. X represents the input feature vector, extracted from the current deviation curve trend data, including trend direction, rate of change, and periodic feature values. The feature vector is standardized to ensure numerical consistency. b represents the bias term, also learned through model training, used to adjust the probability calculation baseline to adapt to the inherent characteristics of the equipment or the operating background. The formula is designed based on the logistic regression framework, using the Sigmoid function to map the linear output to probability values. The module applies this formula in real-time prediction to generate probability estimates of deterioration events for each component within a specified future time period, and uses it as the decision input for the safety threshold comparison module.

[0065] In summary, by collecting equipment operating parameters in real time through sensors and combining them with preset standard parameters and dynamically updated abnormal thresholds, the system can accurately identify the deviation between the operating status and the standard, and use time series analysis technology to identify the deviation trend, thereby achieving quantitative evaluation and prediction of the operating status.

[0066] By introducing strong learning algorithms to build a state prediction model, the probability of future deterioration events such as wear and overheating of components can be predicted based on historical data, enabling early warning of faults. Furthermore, through a real-time alarm mechanism and maintenance knowledge base, alarms are automatically triggered and targeted maintenance suggestions are generated when potential risks are identified, supporting the efficient allocation and tracking of maintenance tasks.

[0067] Overall, this invention achieves intelligent management of the entire process from data acquisition, status monitoring, trend analysis, fault prediction to maintenance decision-making, improving the initiative, accuracy and automation level of equipment maintenance in power plants, helping to reduce the risk of sudden failures, optimize maintenance resource allocation and extend equipment service life.

[0068] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A preventive maintenance monitoring system for power plant areas based on digital twins and equipment condition prediction, characterized in that, include: The real-time data acquisition module is used to periodically acquire real-time operating parameters of the current status of each operating component of the electrical equipment through sensors; The standard parameter storage module is used to store the preset standard operating parameters of each operating component of the electrical equipment under normal operating conditions and their abnormal alarm thresholds. The deviation calculation module is used to compare the real-time operating parameters with the standard operating parameters of the corresponding operating components, calculate the difference, obtain the deviation value between the real-time operating data of the operating component in each state and the standard operating parameters, and generate a deviation value sequence. The trend recognition module is used to identify the trend of the deviation curve of the device within the current period, including the trend direction, rate of change and periodic characteristics, based on the deviation value sequence and using time series analysis, and outputs trend indicators; The model building module is used to build state prediction models using strong learning algorithms, and trains the models with historical deviation data and deterioration event labels. The event prediction module is used to extract the state prediction model trained by the model building module. The deviation curve trend of each operating component of the current equipment is input into the state prediction model to predict the probability of one or more deterioration events corresponding to each component within a specified time period in the future. Deterioration events include component wear, overheating, insulation aging or mechanical failure. The threshold comparison module is used to compare the probability of the occurrence of the deterioration event with a preset safety threshold, which is set based on the critical state of the equipment or the cost-effectiveness of maintenance, and to identify deterioration events that exceed the safety threshold. The measure matching module is used to match related maintenance measures, including component replacement, lubrication, parameter adjustment, or shutdown for maintenance, from the maintenance knowledge base based on the probability of deterioration events exceeding the safety threshold, and generate maintenance suggestion plans.

2. The power plant preventive maintenance monitoring system based on digital twin and equipment status prediction according to claim 1, characterized in that, The real-time operating parameters in the real-time data acquisition module include at least one or more of temperature, vibration, current, voltage, and power factor, and the real-time operating parameters are transmitted to the management terminal for filtering, noise reduction, or normalization processing.

3. The power plant preventive maintenance monitoring system based on digital twin and equipment status prediction according to claim 1, characterized in that, The standard parameter storage module supports a dynamic update mechanism, which periodically extracts new standard parameters and abnormal alarm thresholds from historical operating data, verifies their effectiveness, and then updates them.

4. The power plant preventive maintenance monitoring system based on digital twin and equipment status prediction according to claim 1, characterized in that, The process by which the trend recognition module identifies the trend of the deviation curve is as follows: Receive the deviation value sequence and construct a discrete time series dataset at fixed time intervals; perform a stationarity test on the dataset, and if the sequence is non-stationary, convert it into a stationary time series through difference operations or logarithmic transformation methods; It is used to fit stationary time series using an autoregressive integral moving average model, capture the autocorrelation, trend and periodic components in the series, and construct a time series model; Based on the fitted time series model, the predicted deviation values ​​for one or more future time points are calculated and connected to form a predicted trajectory. By performing first derivative analysis on the predicted trajectory, the direction of the deviation curve trend is quantified as rising, falling, or stable. The strength and rate of change of the trend are quantified by calculating the slope of the linear regression or the rate of change of the moving average.

5. The power plant preventive maintenance monitoring system based on digital twin and equipment status prediction according to claim 1, characterized in that, The trend recognition module is connected to the judgment module via a wireless network. The judgment module is used to compare the deviation value obtained by the trend recognition module with the abnormal alarm threshold preset by the standard parameter storage module in real time. The abnormal alarm threshold is dynamically adjusted based on the equipment safety standard or historical fault data, and an alarm signal is triggered when the deviation value exceeds the abnormal alarm threshold.

6. The power plant preventive maintenance monitoring system based on digital twin and equipment status prediction according to claim 1, characterized in that, The process of constructing the state prediction model in the model construction module includes: Collect the deviation value sequence of each operating component of the electrical equipment and the corresponding deterioration event label data from the historical database; Strong learning algorithm was selected as the basic framework of the model. The selected algorithm was initialized by setting the number of network layers, number of nodes and activation function type. Iterative training was carried out through forward and backward propagation mechanisms, using historical deviation value sequence as input features and deterioration event labels as supervision targets to minimize prediction error. The model performance is evaluated using a validation dataset. By calculating accuracy, recall, and confusion matrix metrics, overfitting or underfitting issues are identified, and regularization techniques or early stopping methods are used to optimize the model's generalization ability.

7. The power plant preventive maintenance monitoring system based on digital twin and equipment status prediction according to claim 1, characterized in that, The formula for calculating the probability of a certain deteriorating event in the event prediction module is as follows: In the formula, P(E|D) represents the probability of the deterioration event E occurring given the current deviation curve trend data D, exp represents the natural exponential function, w represents the weight vector, X represents the input feature vector, and b represents the bias term.

8. The power plant preventive maintenance monitoring system based on digital twin and equipment status prediction according to claim 1, characterized in that, The measure matching module submits the maintenance suggestion plan to the management end through API interface or message queue, and supports the allocation and tracking of maintenance tasks.

9. The power plant preventive maintenance monitoring system based on digital twin and equipment status prediction according to claim 5, characterized in that, The judgment module is interconnected with the alarm execution module via a wireless network. The alarm execution module is used to respond to the alarm signal and immediately alarm via an audible and visual alarm, SMS notification, or pop-up window on the management platform, and record the alarm time, device, and component information.

10. The power plant preventive maintenance monitoring system based on digital twin and equipment status prediction according to claim 1, characterized in that, The deviation calculation module is interconnected with the real-time data acquisition module, the standard parameter storage module, and the trend recognition module via a wireless network. The model building module is interconnected with the trend recognition module and the event prediction module via a wireless network. The threshold comparison module is interconnected with the event prediction module and the measure matching module via a wireless network.

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