Power equipment fault prediction system based on artificial intelligence

Abstract

The invention relates to the technical field of artificial intelligence, and discloses a power equipment fault prediction system based on artificial intelligence. According to the system, through real-time data acquisition and intelligent data preprocessing, the operation state, environment information and historical fault records of the power equipment can be comprehensively acquired, so that dynamic monitoring of the equipment state is realized, and different from a traditional passive maintenance mode depending on regular maintenance and manual inspection, the system can timely identify potential faults and improve the maintenance efficiency. According to the method, the possibility of fault occurrence is accurately predicted, the risk of sudden equipment faults is reduced, power failure and economic loss caused by the equipment faults are effectively reduced, the operation and maintenance cost is remarkably reduced, meanwhile, operation and maintenance personnel can take timely maintenance actions according to early warning information and response measures generated by the system, and the maintenance efficiency is improved. According to the invention, the safe and stable operation of the power system is guaranteed, and in addition, through the active management mode, the service life of the equipment is prolonged, and the operation efficiency of the whole power system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence, and particularly to a power equipment fault prediction system based on artificial intelligence. Background Art

[0002] In modern society, as an important part of the infrastructure, power equipment plays a key role in power production, transmission and distribution. With the rapid economic development and the acceleration of urbanization, the power demand is increasing continuously, and the application scope of various power equipment is also becoming increasingly wide, covering multiple links such as power plants, substations, distribution networks and end-users. The safe and stable operation of these equipment is directly related to the reliability and economy of the power system. Therefore, ensuring the healthy state of power equipment is particularly important.

[0003] Traditional equipment maintenance methods often rely on regular inspections and manual checks, which are difficult to monitor the equipment status in real time and have insufficient ability to predict potential faults. This passive maintenance mode not only increases the operation and maintenance costs, but also may lead to sudden equipment failures, thus affecting the safety and stable operation of the power system. Summary of the Invention

[0004] (1) Technical Problems to be Solved

[0005] Aiming at the deficiencies of the prior art, the present invention provides a power equipment fault prediction system based on artificial intelligence. Through real-time data collection and intelligent data preprocessing, it can comprehensively obtain the operation status, environmental information and historical fault records of power equipment, so as to realize the dynamic monitoring of equipment status. Different from the traditional passive maintenance mode that relies on regular inspections and manual checks, this system can timely identify potential faults, accurately predict the possibility of fault occurrence, reduce the risk of sudden equipment failures, not only effectively reduce the power outages and economic losses caused by equipment failures, but also significantly reduce the operation and maintenance costs. At the same time, operation and maintenance personnel can take timely maintenance actions according to the warning information and response measures generated by the system, ensuring the safety and stable operation of the power system. In addition, through this proactive management method, the service life of the equipment is extended and the operation efficiency of the entire power system is improved.

[0006] (2) Technical Solutions

[0007] To achieve the above object, the present invention provides the following technical solutions: A power equipment fault prediction system based on artificial intelligence, including a data collection module, a data preprocessing module, a model training module, a fault prediction module and a warning response module;

[0008] The data acquisition module obtains device operation status data, environmental data, historical fault records, device service life, and device load historical data from power equipment, and transmits the obtained data to the data preprocessing module;

[0009] The data preprocessing module preprocesses the data obtained by the data acquisition module, including data denoising, outlier removal, missing value filling, duplicate data resolution, and data verification, and extracts the vibration amplitude characteristics and maximum load characteristics of the power equipment and transmits them to the model training module;

[0010] The model training module trains a power equipment fault prediction model using machine learning algorithms based on the processed data and the extracted features, and evaluates the accuracy and generalization ability of the model through cross-validation;

[0011] The fault prediction module performs power equipment fault prediction based on the trained power equipment fault prediction model, and at the same time transmits the prediction result to the early warning response module;

[0012] The early warning response module generates early warning information according to the prediction result, formulates response measures, and generates an early warning level to be pushed to the power equipment operation and maintenance personnel.

[0013] Preferably, the formula for data denoising is as follows:

[0014]

[0015] In the formula, SMA t represents the smoothed value at time t, x t-i represents the value of the original data at time t-i, N represents the window size, and i represents the subscript index.

[0016] Preferably, the formula for outlier removal is as follows:

[0017]

[0018] In the formula, Z represents the Z-score value, x represents the value of the data to be processed, μ represents the mean of the data to be processed, and σ represents the standard deviation of the data to be processed.

[0019] Preferably, the formula for filling missing values is as follows:

[0020]

[0021] In the formula, x′ represents the filled value, μx represents the mean of the non-missing data, and x represents the data for which the missing value needs to be identified.

[0022] Preferably, the formula for resolving duplicate data is as follows:

[0023]

[0024] In the formula, D represents the proportion of duplicate data, R represents the number of duplicate data, and T represents the total number of data.

[0025] Preferably, the formula for data verification is as follows:

[0026]

[0027] In the formula, I represents the data integrity ratio, V represents the number of data passing the verification, and T represents the total number of data.

[0028] Preferably, the formula for extracting the vibration amplitude characteristics of power equipment is as follows:

[0029]

[0030] In the formula, μ v represents the mean value of the vibration amplitude characteristics of power equipment, M represents the total number of vibration data points of power equipment, and v j represents the jth vibration data point.

[0031] Preferably, the formula for extracting the maximum load characteristics of power equipment is as follows:

[0032]

[0033] In the formula, Load max represents the maximum load characteristics of power equipment, and l i represents the ith load data point.

[0034] Preferably, the power equipment fault prediction model is as follows:

[0035] yc = w0 + w1 * x1 + w2 * x2 +... + wn * xn

[0036] In the formula, yc represents the predicted fault probability of power equipment, w0 represents the bias term, w1, w2,..., wn represent the weights of the characteristics, which are dynamically assigned by artificial intelligence, and x1, x2,..., xn represent the values of the input characteristics.

[0037] Preferably, the formula for the cross - validation evaluation model is as follows:

[0038]

[0039] In the formula, CV represents the average accuracy of cross - validation, A k represents the accuracy of the kth fold, and K represents the total number of folds.

[0040] Compared with the prior art, the present invention provides an artificial intelligence-based power equipment fault prediction system, which has the following beneficial effects:

[0041] Through real-time data collection and intelligent data preprocessing, the present invention can comprehensively obtain the operating status, environmental information and historical fault records of power equipment, so as to realize the dynamic monitoring of equipment status. Different from the traditional passive maintenance mode that relies on regular maintenance and manual inspection, this system can timely identify potential faults, accurately predict the possibility of fault occurrence, reduce the risk of sudden equipment failures, not only effectively reduce the power outages and economic losses caused by equipment failures, but also significantly reduce the operation and maintenance costs. At the same time, operation and maintenance personnel can take timely maintenance actions according to the warning information and response measures generated by the system, ensuring the safe and stable operation of the power system. In addition, through this proactive management method, the service life of the equipment is extended, and the operation efficiency of the entire power system is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0044] Aiming at the problem that the traditional equipment maintenance method often relies on regular maintenance and manual inspection, it is difficult to monitor the equipment status in real time, and the ability to predict potential faults is insufficient. This passive maintenance mode not only increases the operation and maintenance costs, but also may lead to sudden equipment failures, thus affecting the safety and stable operation of the power system. For this reason, an artificial intelligence-based power equipment fault prediction system is proposed. Please refer to Figure 1 ,. The system includes a data collection module, a data preprocessing module, a model training module, a fault prediction module and a warning response module;

[0045] As the first step of the power equipment fault prediction system, the data collection module uses advanced sensors and Internet of Things (IoT) technologies to real-time obtain the operating status data of the equipment through an integrated sensor network. These sensors can monitor multiple indicators, such as key parameters like current, voltage, temperature, vibration and frequency, and transmit the data at high speed. At the same time, environmental data, such as humidity, temperature and air pressure, are also collected by environmental monitoring equipment to evaluate the impact of external factors on equipment performance;

[0046] The module also integrates the historical fault records and service life information of the equipment. By conducting real-time docking with the data interface of the equipment management system, it obtains the past fault occurrence time, fault types, and their repair records. In addition, by analyzing the historical data of the equipment load, the system can track the operation of the equipment under different loads to identify potential overload risks. During the data collection process, data compression and encryption technologies are adopted to ensure the security and efficiency of transmission, enabling all the acquired data to be seamlessly transmitted to the data preprocessing module, laying a solid foundation for subsequent data cleaning and feature extraction. In this way, a complete data collection chain can efficiently and accurately collect important data of power equipment, providing reliable basic data support for fault prediction;

[0047] The data preprocessing module is a key link in the power equipment fault prediction system. It is responsible for comprehensively sorting and cleaning the raw data from the data collection module to ensure the accuracy and effectiveness of subsequent processing and analysis. First, the module cleans the collected data through data denoising technology, using the simple moving average (SMA) method:

[0048]

[0049] where SMA t represents the smoothed value at time t, and N is the window size. This technology can effectively eliminate random noise, making the data smoother and more coherent, thus better reflecting the true operating state of the power equipment;

[0050] Next, the module performs outlier detection and uses the Z-score method to remove outliers. The formula is:

[0051]

[0052] When the absolute value of Z is greater than 3, this data point will be regarded as an outlier. This method helps to automatically identify and eliminate data that does not conform to the normal fluctuation range, thus avoiding the negative impact of abnormal data on subsequent models;

[0053] Subsequently, the module deals with missing value imputation, using the mean imputation technique. The formula is:

[0054]

[0055] By replacing the missing data with the mean, the module can maintain the integrity of the dataset, prevent model training bias caused by missing values, and thus improve the reliability of prediction;

[0056] When dealing with duplicate data, the module calculates the proportion of duplicate data, using the formula:

[0057]

[0058] By identifying and removing duplicate records, the uniqueness and consistency of the data can be ensured, thereby improving the accuracy of the analysis results;

[0059] In the data verification phase, the module performs an integrity check using the formula:

[0060]

[0061] where V represents the amount of data that passes the verification, ensuring the quality and accuracy of the data used in subsequent steps. Finally, the module extracts the characteristic data of the power equipment, including the vibration amplitude characteristic and the maximum load characteristic. For the vibration amplitude, the formula is:

[0062]

[0063] For the load characteristic, the formula is:

[0064]

[0065] Through these feature extractions, the data preprocessing module can effectively provide more representative and informative data for the model training module. The optimized and cleaned data can not only improve the training efficiency of the model, but also help enhance the generalization ability of the fault prediction model, improving the system's adaptability and accuracy to unknown data. The implementation of this series of processes ensures the accuracy and reliability of the entire fault prediction system, providing support for the operation and maintenance of power equipment;

[0066] The model training module plays a crucial role in the power equipment fault prediction system. Based on the data sorted out and the features extracted during the data preprocessing stage, this module uses a variety of advanced machine learning algorithms (such as decision trees, random forests, support vector machines, or neural networks, etc.) for model training. Selecting the appropriate algorithm is crucial for capturing the complex relationships between different features and fault patterns. For example, when using linear regression for fault prediction, it can be expressed as:

[0067] yc = w0 + w1 * x1 + w2 * x2 +... + wn * xn

[0068] Here, yc represents the predicted fault type or probability, while w0 to wn are the weights of the model, and x1 to xn are the extracted feature values. This way of linear combination can quickly adjust the model parameters to minimize the prediction error and make the prediction results more accurate;

[0069] To evaluate the accuracy and generalization ability of the model, this module uses the cross - validation technique. By dividing the dataset into k subsets for verification, the following formula is used to calculate the average accuracy of the model:

[0070]

[0071] Here, A k represents the accuracy obtained from the K-fold cross-validation, where K is the number of folds. By this method, the system can effectively evaluate the performance of the model on different training sets and validation sets, ensuring that the model can not only make good predictions on the training data but also be applicable to new and unseen data. This process is particularly important for improving the robustness and reliability of the model, enabling it to effectively reduce the risks of false alarms and missed detections in practical applications;

[0072] Once the training is completed, the fault prediction module uses the trained model for real-time fault prediction. This module receives the latest operating data of the device in real-time and, through the same feature extraction steps, inputs this data into the fault prediction model for analysis. The prediction results not only include the possible fault types but also provide the probability of the fault occurrence, helping the operation and maintenance personnel to identify potential risks and faults in advance;

[0073] Finally, the warning response module is responsible for converting the results of the fault prediction into actionable warning messages. Based on the prediction results, the module generates warning messages of different levels and formulates corresponding response measures according to their severity. The generation of the warning level is usually classified according to the set thresholds. For example, when the fault probability exceeds a certain critical value, the system automatically marks the fault as high risk and sends the relevant information to the operation and maintenance personnel. This mechanism can not only improve the response efficiency of the operation and maintenance personnel but also effectively reduce the impact of potential faults on the operation of the power system, thus ensuring the safe and stable operation of the power equipment;

[0074] The entire process combines data-driven methods with real-time operations, accelerating the fault response speed and enhancing the intelligent level of power equipment management, providing a guarantee for improving the reliability and efficiency of the power system.

[0075] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An artificial intelligence-based power equipment fault prediction system, characterized in that, It includes a data acquisition module, a data preprocessing module, a model training module, a fault prediction module, and an early warning response module; The data acquisition module obtains device operation status data, environmental data, historical fault records, device service life, and device load historical data from power equipment, and transmits the obtained data to the data preprocessing module; The data preprocessing module preprocesses the data obtained by the data acquisition module, including data denoising, outlier removal, missing value filling, duplicate data resolution, and data verification, and extracts the vibration amplitude feature and the maximum load feature of the power equipment and transmits them to the model training module; The model training module trains a power equipment fault prediction model using machine learning algorithms based on the processed data and the extracted features, and evaluates the accuracy and generalization ability of the model through cross-validation; The fault prediction module performs power equipment fault prediction based on the trained power equipment fault prediction model, and at the same time transmits the prediction result to the early warning response module; The early warning response module generates early warning information according to the prediction result, formulates response measures, and generates an early warning level and pushes it to the power equipment operation and maintenance personnel.

2. The power equipment fault prediction system based on artificial intelligence according to claim 1, characterized in that: The formula for the data denoising is as follows: In the formula, SMA t represents the smoothed value at time t, x t-i represents the value of the original data at time t - i, N represents the window size, and i represents the subscript index.

3. The power equipment fault prediction system based on artificial intelligence according to claim 2, characterized in that: The formula for the outlier removal is as follows: In the formula, Z represents the Z-score value, x represents the value of the data to be processed, μ represents the mean of the data to be processed, and σ represents the standard deviation of the data to be processed.

4. An artificial intelligence-based power equipment fault prediction system according to claim 3, characterized in that: The formula for the missing value filling is as follows: In the formula, x′ represents the filled value, μx represents the mean of the non-missing data, and x represents the data for which the missing value needs to be identified.

5. An artificial intelligence-based power equipment fault prediction system according to claim 4, characterized in that: The formula for the duplicate data resolution is as follows: In the formula, D represents the duplicate data ratio, R represents the number of duplicate data, and T represents the total number of data.

6. The power equipment fault prediction system based on artificial intelligence according to claim 5, characterized in that: The formula for the data verification is as follows: In the formula, I represents the data integrity ratio, V represents the number of data passing the verification, and T represents the total number of data.

7. The power equipment fault prediction system based on artificial intelligence according to claim 6, characterized in that: The formula for extracting the vibration amplitude feature of the power equipment is as follows: In the formula, μ v represents the mean value of the vibration amplitude characteristics of the power equipment, M represents the total number of vibration data points of the power equipment, and v j represents the j-th vibration data point.

8. An artificial intelligence-based power equipment fault prediction system according to claim 7, characterized in that: The formula for extracting the maximum load feature of the power equipment is as follows: In the formula, Load max represents the maximum load characteristic of the power equipment, and l i represents the i-th load data point.

9. An artificial intelligence-based power equipment fault prediction system according to claim 8, characterized in that: The power equipment fault prediction model is as follows: yc = w0 + w1 * x1 + w2 * x2 +... + wn * xn In the formula, yc represents the predicted fault probability of the power equipment, w0 represents the bias term, w1, w2,..., wn represent the weights of the features, which are dynamically assigned by artificial intelligence, and x1, x2,..., xn represent the values of the input features.

10. An artificial intelligence-based power equipment fault prediction system according to claim 9, characterized in that: The formula for evaluating the model through cross-validation is as follows: In the formula, CV represents the average accuracy of cross-validation, and A k represents the accuracy of the k-th fold, and K represents the total number of folds.

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