A Flexible Anode Fault Detection Method and System

Abstract

The present invention discloses a method and system for detecting faults in a flexible anode, which relates to the technical field of intelligent monitoring and fault diagnosis. The method includes: collecting the operation data of the flexible anode in real time and preprocessing the collected operation data; constructing a fault prediction model based on the preprocessed data to predict the faults of the flexible anode; identifying the faults of the flexible anode according to the prediction results; emitting a detection signal to the flexible anode based on the analysis results, and detecting the fault points of the flexible anode by analyzing the reflection mode of the signal; and generating a fault detection report by combining the fault point detection results of the flexible anode and the prediction results of the fault prediction model. By constructing a fault detection system for the flexible anode, the present invention integrates real-time data collection, intelligent analysis and prediction, and precise positioning, significantly enhancing the system safety, improving the maintenance efficiency, effectively reducing the cost, promoting continuous optimization and upgrading, and comprehensively ensuring the stability and high efficiency of industrial operation.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent monitoring and fault diagnosis, and particularly to a method and system for detecting faults in flexible anodes. Background Art

[0002] As an important part of the cathodic protection system, the flexible anode technology has been widely used in recent years in fields such as ocean engineering, oil and gas pipelines, and the chemical industry, and is favored for its excellent flexibility and adaptability. With the rapid development of Internet of Things, big data, and artificial intelligence technologies, the status of intelligent monitoring systems in the industrial field has become increasingly prominent. In the field of fault detection of flexible anodes, traditional detection methods mainly rely on manual inspections or periodic maintenance, which are inefficient and easily affected by subjective factors, and it is difficult to meet the requirements of modern industrial production for high reliability and instant response. In recent years, data-driven fault prediction based on sensor networks has gradually become a research hotspot, which realizes early warning of potential faults by real-time monitoring of the structural health status.

[0003] However, the current flexible anode fault detection technology still has obvious deficiencies. On the one hand, most systems only rely on a single type of sensor for data collection, and cannot comprehensively capture the multi-dimensional information of the flexible anode in a complex working environment, resulting in limited accuracy and coverage of fault detection. On the other hand, the existing technology lacks effective noise suppression and feature extraction means in the data preprocessing stage, so that the noise and redundant information in the original data directly affect the accuracy of subsequent fault prediction. More critically, traditional fault prediction models are often limited to static analysis and are difficult to capture the dynamic process and long-term trend of flexible anode faults, especially when dealing with non-linear and complex fault modes, they perform poorly. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method and system for detecting faults in flexible anodes to solve the problems of single sensor information, insufficient data preprocessing, static limitation of prediction models, low fault detection accuracy, small coverage, inaccurate prediction, and low efficiency.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for detecting faults in a flexible anode, which includes collecting the operation data of the flexible anode in real time and preprocessing the collected operation data; constructing a fault prediction model based on the preprocessed data to predict the faults of the flexible anode; identifying the faults of the flexible anode according to the prediction results; emitting a detection signal to the flexible anode based on the analysis results, and detecting the fault point of the flexible anode by analyzing the reflection mode of the signal; and generating a fault detection report by combining the fault point detection result of the flexible anode and the prediction result of the fault prediction model.

[0008] As a preferred solution of the method for detecting faults in the flexible anode according to the present invention, wherein: the specific steps of the real-time collection of the operation data of the flexible anode are as follows,

[0009] Deploy various types of sensors at key positions of the flexible anode, including temperature sensors, current sensors, voltage sensors, and fiber Bragg grating sensors;

[0010] Collect the operation data of the flexible anode in real time through the deployed various types of sensors.

[0011] As a preferred solution of the method for detecting faults in the flexible anode according to the present invention, wherein: the specific steps of the preprocessing of the collected operation data are as follows,

[0012] Perform multi-scale analysis using wavelet transform, decompose the signal into different frequency components, and remove high-frequency random noise while retaining useful information;

[0013] Use a time series model to predict and fill in missing values;

[0014] Use Z-score standardization to convert the operation data to the same magnitude;

[0015] Use FFT to convert the time signal into a spectrum, analyze the frequency components of the signal, and extract the main frequency and frequency band energy characteristics;

[0016] Take each extracted main frequency and frequency band feature as a dimension, and form a multi-dimensional feature space by horizontal splicing.

[0017] As a preferred solution of the method for detecting faults in the flexible anode according to the present invention, wherein: the specific steps of constructing a fault prediction model based on the preprocessed data to predict the faults of the flexible anode are as follows,

[0018] Adopt a long short-term memory network to construct a fault prediction model;

[0019] Divide the preprocessed data set into a training set, a validation set, and a test set;

[0020] Use the training set data to train the model, optimize the model weights through the backpropagation algorithm, and minimize the loss function;

[0021] Evaluate the model performance on the validation set and adjust the hyperparameters;

[0022] Evaluate the generalization ability of the model on an independent test set;

[0023] Input the preprocessed dataset into the trained fault prediction model to predict the fault probability of the flexible anode. The expression is:

[0024] y(t) = σ(W out ·S(D(t)) + b out );

[0025] Where, represents the probability of the flexible anode failing at time point t, W out is the weight matrix of the fully connected layer, b out is the bias vector of the fully connected layer, σ represents the sigmoid function, D(t) represents the preprocessed dataset at time point t, and S is a feature transformation function.

[0026] As a preferred solution of the flexible anode fault detection method described in the present invention, wherein: according to the prediction result, perform fault identification on the flexible anode. The specific steps are as follows,

[0027] Based on the analysis of the fault records in the historical data, define a safety warning threshold T. By comparing the probability of the flexible anode failing with the safety warning threshold T, perform fault identification;

[0028] When , it is considered that the flexible anode has no fault;

[0029] When , it is considered that the flexible anode has a fault.

[0030] As a preferred solution of the flexible anode fault detection method described in the present invention, wherein: based on the analysis result, emit a detection signal to the flexible anode, and detect the fault point of the flexible anode by analyzing the reflection pattern of the signal. The specific steps are as follows,

[0031] When it is identified that the flexible anode has a fault, use an electromagnetic signal transmitter to emit a detection signal to the flexible anode;

[0032] Use a receiving device to receive the detection signal reflected from the flexible anode carrying information about the anode structure and state, and divide the received reflected signal into multiple sub-signal segments, with each signal segment corresponding to a different physical location of the flexible anode;

[0033] Design a position mapping function to determine the specific location of the fault point and convert the high-dimensional feature space into interpretable physical preliminary position coordinates. The expression is as follows:

[0034]

[0035] where z(t) is the position feature vector of the flexible anode fault point at time point t, W loc is the weight matrix of the position mapping layer, b loc is the bias vector of the position mapping layer, P(z(t)) represents the fault point position coordinates mapped by the position mapping function P based on the position feature vector z(t) at time point t, V is the propagation speed of the detection signal in the flexible anode, and Δt is the time difference between the transmission and reflection of the detection signal;

[0036] Import the fault point position coordinates provided by P(z(t)) into the signal analysis system as the key area for analysis;

[0037] Use FFT to extract the frequency features of the signal, observe whether there are new frequency components and the intensity changes of the original components, compare the extracted signal features with the historical fault mode data to find similar feature patterns, and combine the position coordinates provided by P(z(t)) and the signal analysis results to generate the final fault point position.

[0038] As a preferred solution of the flexible anode fault detection method described in the present invention, wherein: the fault detection report includes the time, location of the fault occurrence, and the systems and components involved;

[0039] Deeply analyze the root cause of the fault and provide a detailed fault cause analysis;

[0040] The report gives detailed treatment measures, including emergency response steps, fault repair processes, and corrective measures taken, and at the same time puts forward prevention and improvement suggestions.

[0041] In a second aspect, the present invention provides a flexible anode fault detection system, including a data acquisition and preprocessing module, a fault prediction module, a fault identification module, a fault point location module, and a fault detection report generation module; the data acquisition and preprocessing module is used to collect the operation data of the flexible anode in real time and preprocess the collected operation data; the fault prediction module is used to build a fault prediction model based on the preprocessed data and predict the fault of the flexible anode; the fault identification module is used to identify the fault of the flexible anode according to the prediction result; the fault point location module is used to emit a detection signal to the flexible anode based on the analysis result and detect the fault point of the flexible anode by analyzing the reflection mode of the signal; the fault detection report generation module is used to generate a fault detection report by combining the fault point detection result of the flexible anode and the prediction result of the fault prediction model.

[0042] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, any step of the flexible anode fault detection method described in the first aspect of the present invention is implemented.

[0043] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, any step of the flexible anode fault detection method described in the first aspect of the present invention is implemented.

[0044] The beneficial effects of the present invention are as follows: By collecting and preprocessing the operation data of the flexible anode in real time, the comprehensive monitoring of its operation state and the improvement of data quality are realized. Furthermore, a fault prediction model is constructed based on the processed data, realizing the early warning of potential faults and improving the preventive maintenance ability of the system. Then, fault identification is carried out according to the prediction result to ensure the accuracy of fault judgment. Moreover, the fault point is accurately located by transmitting a detection signal and analyzing the reflection mode, enhancing the accuracy and response speed of fault diagnosis. Finally, a detailed fault report is generated based on the comprehensive fault detection result, providing fault handling measures and preventive suggestions, and overall improving the safety and reliability of the flexible anode system. Description of the Drawings

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 It is a flowchart of the flexible anode fault detection method in Embodiment 1.

[0047] Figure 2 It is a flowchart of the flexible anode fault detection system in Embodiment 1. Detailed Embodiments

[0048] To make the above objects, features, and advantages of the present invention more obvious and understandable, the detailed embodiments of the present invention will be described in detail below with reference to the drawings of the specification.

[0049] Many specific details are set forth in the following description to facilitate a full understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0050] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0051] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a flexible anode fault detection method, comprising the following steps:

[0052] S1. Collect the operation data of the flexible anode in real time and pre-process the collected operation data.

[0053] Furthermore, multiple types of sensors are deployed at key locations of the flexible anode, including temperature sensors, current sensors, voltage sensors, and fiber Bragg grating sensors for structural health monitoring. The design of the sensor network takes into account the geometry and material properties of the anode to ensure that all potential high-prone areas of failure are covered;

[0054] By deploying various types of sensors, the operating data of the flexible anode is collected in real time, including temperature, current, and voltage data.

[0055] It should be noted that key locations refer specifically to those specific areas that have a decisive influence on the overall performance, safety and long-term reliability of the anode. These locations include but are not limited to the locations with the highest current density, the greatest mechanical stress, the most drastic temperature changes, the strongest chemical activity and the closest connection to other structural parts. By deploying sensors such as temperature, current, voltage and fiber Bragg gratings at these points, it is possible to fully monitor the health of the flexible anode and promptly capture subtle changes that may indicate early failures, thereby providing important data support for preventive maintenance and ensuring the stability and safety of system operation.

[0056] Wavelet transform is used for multi-scale analysis to decompose the signal into different frequency components, retaining useful information while removing high-frequency random noise;

[0057] Fill missing values ​​using time series model predictions;

[0058] It should be noted that, first, the time series data is analyzed to identify the missing values. Then, a suitable time series prediction model is selected, such as autoregressive (AR), moving average (MA), autoregressive integrated moving average (ARIMA), or long short-term memory network (LSTM), etc. These models can predict future trends based on the existing time series data. By learning from historical data, the model can calculate the possible values at the missing time points. After the model training is completed, it is applied to the positions where the missing values are located to predict and fill these missing values. This method can maintain the time continuity of the data and provide a more complete and accurate data set for subsequent fault prediction.

[0059] Use Z-score normalization to convert the operating data to the same magnitude for convenient model training;

[0060] It should be noted that, first, the mean and standard deviation of each group of data are calculated. Then, each observation value is normalized. In this way, the data with different magnitudes and units are all converted to a standard normal distribution with a mean of 0 and a standard deviation of 1. This normalization process eliminates the influence of the dimension, enabling effective comparison and fusion of different types of sensor data on the same scale, and improving the accuracy and reliability of subsequent analysis.

[0061] Use FFT to convert the time signal into a spectrum, analyze the frequency components of the signal, and extract the main frequency and frequency band energy characteristics;

[0062] It should also be noted that this process first applies FFT to the original time signal to obtain a spectrogram, which contains the amplitude information of the signal at each frequency component. By analyzing the spectrogram, the main frequency of the signal, that is, the frequency component with the largest energy in the signal, and the energy distribution of different frequency bands can be identified. The main frequency and frequency band energy characteristics are key indicators in signal analysis. They can reveal whether there are periodic patterns in the signal, whether it is affected by specific frequency noise, and the health state or fault mode of the signal. This information is crucial for fault prediction and diagnosis, and can help identify possible problems in the flexible anode, such as material fatigue, cracks, or corrosion, etc., so as to guide maintenance decisions.

[0063] Each extracted main frequency and frequency band feature is used as a dimension, and a multi-dimensional feature space is constructed by horizontal concatenation. Each point in this space represents the state of the flexible anode at a certain moment;

[0064] S2. Based on the preprocessed data, construct a fault prediction model for fault prediction of the flexible anode.

[0065] Furthermore, considering the complexity and nonlinearity of flexible anode fault prediction, a long short-term memory network is adopted to construct a fault prediction model. The long short-term memory network can effectively capture the long-term dependencies in time series data;

[0066] The preprocessed dataset is divided into a training set, a validation set, and a test set, and the ratio can be set as 70%, 15%, 15%;

[0067] The training set data is used for model training. The model weights are optimized through the backpropagation algorithm to minimize the loss function, such as the mean squared error (MSE), to improve the prediction accuracy of the model;

[0068] It should be noted that the backpropagation algorithm is the core mechanism for optimizing model weights in deep learning and neural networks. After model prediction, the value of the loss function is calculated, which represents the prediction error under the current model parameter configuration. The backpropagation algorithm determines how to adjust the weights to reduce the loss by calculating the gradient of the loss function with respect to each weight (i.e., how the loss function changes as the weights change). This process starts from the output layer and traverses the entire network in reverse until the input layer, calculating the gradient through the chain rule. After obtaining the gradient, the weight update is performed according to a specific learning rate, which determines the magnitude of the weight adjustment. The weight update follows the principle of gradient descent, that is, moving along the negative direction of the loss function gradient until the minimum value of the loss function is found, which means the model has found a set of optimal parameters to minimize the prediction error.

[0069] Minimizing the loss function is the main goal of training a neural network. In each iteration, the model adjusts its parameters according to the calculated gradient, attempting to make the value of the loss function as low as possible. As the training progresses, the prediction ability of the model gradually improves, and the value of the loss function will gradually decrease. Ideally, the training process will converge to a stable minimum point, at which time the model parameters are considered optimal, capable of fitting the training data well and having a certain generalization ability to make accurate predictions on unseen new data.

[0070] Evaluate the model performance on the validation set, adjust the hyperparameters to prevent overfitting and ensure the generalization ability of the model;

[0071] Evaluate the generalization ability of the model on an independent test set to ensure the prediction accuracy of the model on unknown data;

[0072] Input the preprocessed dataset into the trained fault prediction model to predict the fault probability of the flexible anode. The expression is:

[0073] y(t) = σ(W out ·S(D(t)) + b out )

[0074] Among them, represents the probability of the flexible anode failing at time point t, W out is the weight matrix of the fully connected layer, b out is the bias vector of the fully connected layer, σ represents the sigmoid function, D(t) represents the preprocessed dataset at time point t, including temperature, current, voltage, and the measured values of fiber Bragg grating sensors, and S is a feature transformation function;

[0075] It should be noted that the function of S can be divided into two steps: 1. Standardization: This part standardizes the data D(t). The purpose of standardization is to scale the data to zero mean and unit variance, which helps improve numerical stability, accelerate model training, and make the comparison between different features more fair. 2. Nonlinear transformation: After standardization, the data passes through the ReLU activation function. This step is to introduce nonlinearity so that the model can learn and represent more complex feature relationships. The ReLU function sets all negative values to zero while leaving positive values unchanged, which helps solve the limitations of linear models and enables the model to better fit the nonlinear data distribution. The expression is:

[0076]

[0077] Among them, this is the mean value of the dataset D(t), and a is the standard deviation of the dataset D(t)

[0078] Design a position mapping function to parse the position-related feature vector output by the model and determine the specific position of the potential fault point, converting the high-dimensional feature space into interpretable physical position coordinates. The expression is:

[0079] P(z(t)) = L·argmax(ReLU(W loc ·z(t) + b loc ));

[0080] Among them, z(t) is the position feature vector of the flexible anode fault point at time point t, W loc is the weight matrix of the position mapping layer, b loc is the bias vector of the position mapping layer, L is the mapping matrix that converts the index to the actual position coordinates, and P(z(t)) represents the fault point position coordinates mapped by the position mapping function P based on the position feature vector z(t) at time point t.

[0081] It should also be noted that the mapping function P is based on the position feature vector z(t) at time point t, through the weight matrix W loc and the bias vector b locPerform a linear transformation, and then apply the ReLU activation function to highlight key features and eliminate negative values. Next, find the index of the maximum value in the transformed feature vector by calculating the "argmax" function, which corresponds to the potential location of the fault point. Finally, use the mapping matrix L to convert this index into the actual physical coordinates, thereby obtaining the specific location of the fault point on the flexible anode and achieving an accurate conversion from the high-dimensional feature space to the interpretable physical coordinates.

[0082] S3. According to the prediction results, perform fault identification on the flexible anode.

[0083] Furthermore, based on the analysis of the fault records in the historical data, define a safety warning threshold T, and perform fault identification by comparing with T;

[0084] When it is considered that the flexible anode has no fault;

[0085] When it is considered that the flexible anode has a fault;

[0086] It should also be noted that the specific value range of the safety warning threshold T depends on the characteristics of the flexible anode system for specific applications and the statistical characteristics of the selected features. For example, if a feature follows a normal distribution under normal operating conditions, T may be set to the mean of the feature plus 3 times the standard deviation, which means that approximately 99.7% of the normal data points will fall below T, thereby controlling the false alarm rate at a low level. In actual operation, T may be fine-tuned according to on-site test and maintenance experience to adapt to specific operating conditions and maintenance strategies.

[0087] S4. Based on the analysis results, emit a detection signal to the flexible anode, and detect the fault point of the flexible anode by analyzing the reflection pattern of the signal.

[0088] Furthermore, when it is identified that the flexible anode has a fault, emit a detection signal to the flexible anode through an electromagnetic signal transmitter;

[0089] Use a receiving device to receive the detection signal reflected from the flexible anode and carrying information about the anode structure and state;

[0090] Design a position mapping function to determine the specific location of the fault point and convert the high-dimensional feature space into interpretable physical preliminary position coordinates. The expression is:

[0091]

[0092] where z(t) is the flexible anode fault point position feature vector at time point t, and W loc is the weight matrix of the position mapping layer, and bloc is the bias vector of the position mapping layer, P(z(t)) represents the fault point position coordinates mapped by the position mapping function P based on the position feature vector z(t) at time point t, V is the propagation speed of the detection signal in the flexible anode, and Δt is the time difference between the transmission and reflection of the detection signal.

[0093] Preprocess the received reflected signal, including filtering, noise reduction, and signal enhancement, to eliminate interference and improve signal quality;

[0094] Import the fault point position coordinates provided by P(z(t)) into the signal analysis system as the key analysis area;

[0095] The specific process includes passing the position coordinates P(z(t)) to the signal analysis system, and the system marks these coordinates as the key analysis area to focus on analyzing the signal reflection pattern in this area, so as to more accurately determine the specific position of the fault point.

[0096] Use FFT to extract the frequency characteristics of the signal, observe whether there are new frequency components and the intensity changes of the original components, which are related to material fatigue, crack propagation or corrosion; compare the extracted signal characteristics with the known fault mode database to find similar characteristic patterns to determine the possible fault types (such as cracks, corrosion, wear, etc.) and severity, provide a basis for subsequent maintenance decisions, and further narrow the position range of the fault point by combining the accurate position information provided by P(z(t)) and the signal analysis results.

[0097] It should be noted that first, a signal containing potential fault information is collected, usually an electromagnetic signal. Then, this time-domain signal is converted into a frequency-domain representation through FFT, that is, the signal is decomposed into its various component frequencies. On the spectrogram, each peak corresponds to a specific frequency, and its height reflects the intensity of that frequency component. By carefully examining the spectrum, it can be observed whether there are new frequency components and whether the intensity of the original frequency components has changed. These changes may indicate material fatigue, crack propagation, or corrosion, because faults often introduce additional frequencies or change the amplitudes of existing frequencies in the signal, thus revealing the abnormal state of the flexible anode structure. By carefully analyzing the frequency components, the fault type and severity can be diagnosed more accurately, providing a scientific basis for maintenance decisions.

[0098] S5. Generate a fault detection report by combining the fault point detection results of the flexible anode and the prediction results of the fault prediction model.

[0099] Furthermore, the fault detection report includes the time, location of the fault, and the systems and components involved;

[0100] Deeply analyze the root cause of the failure and provide a detailed failure cause analysis, which helps to identify the source of the problem;

[0101] The report gives detailed handling measures, including emergency response steps, the process of fault repair and corrective measures taken, and at the same time puts forward preventive and improvement suggestions, strategies to avoid the recurrence of similar failures.

[0102] It should also be noted that the failure report not only details the specific details of the failure, such as the occurrence time, precise location and affected system components, but also deeply analyzes the root cause of the failure, aiming to trace the source of the problem. The report includes comprehensive response strategies, from the immediate response to emergencies to the specific steps of fault repair, and then to the implementation of corrective measures to prevent future risks. In addition, the report also prospectively puts forward preventive maintenance suggestions and system optimization plans, aiming to build a long-term mechanism to prevent the recurrence of similar failures and ensure the continuous stability and high efficiency of system operation.

[0103] This embodiment also provides a flexible anode fault detection system, including: a data acquisition and preprocessing module, a fault prediction module, a fault identification module, a fault point location module and a fault detection report generation module;

[0104] The data acquisition and preprocessing module is used to collect the operation data of the flexible anode in real time and preprocess the collected operation data;

[0105] The fault prediction module constructs a fault prediction model based on the preprocessed data to predict the faults of the flexible anode;

[0106] The fault identification module is used to identify the faults of the flexible anode according to the prediction results;

[0107] The fault point location module is used to emit a detection signal to the flexible anode based on the analysis results and detect the fault point of the flexible anode by analyzing the reflection mode of the signal;

[0108] The fault detection report generation module is used to generate a fault detection report by combining the fault point detection results of the flexible anode and the prediction results of the fault prediction model.

[0109] This embodiment also provides a computer device, applicable to the case of the flexible anode fault detection method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the flexible anode fault detection method proposed in the above embodiment.

[0110] The computer device may be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad provided on the outer shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0111] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the flexible anode fault detection method proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0112] In summary, through real-time collection and preprocessing of the operation data of the flexible anode, the present invention realizes comprehensive monitoring of its operation status and improvement of data quality. Furthermore, a fault prediction model is constructed based on the processed data to achieve early warning of potential faults and improve the preventive maintenance ability of the system. Then, fault identification is performed according to the prediction results to ensure the accuracy of fault judgment. Moreover, the fault point is accurately located by transmitting detection signals and analyzing the reflection mode, enhancing the accuracy and response speed of fault diagnosis. Finally, a detailed fault report is generated based on the comprehensive fault detection results, providing fault handling measures and preventive suggestions, and overall improving the safety and reliability of the flexible anode system.

[0113] 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 the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A flexible anode fault detection method, characterized in that: include, Collect the operating data of the flexible anode in real time and pre-process the collected operating data; Based on the preprocessed data, a fault prediction model is constructed to predict the failure of the flexible anode; According to the prediction results, the flexible anode is fault-identified; Based on the analysis result, a detection signal is emitted to the flexible anode, and a fault point of the flexible anode is detected by analyzing the reflection pattern of the signal; Combining the fault point detection results of the flexible anode and the prediction results of the fault prediction model, a fault detection report is generated; Based on the analysis result, a detection signal is emitted to the flexible anode, and the fault point of the flexible anode is detected by analyzing the reflection mode of the signal. The specific steps are as follows: When it is identified that the flexible anode is faulty, a detection signal is transmitted to the flexible anode through an electromagnetic signal transmitter; Using a receiving device, receiving a detection signal reflected from the flexible anode and carrying information about the anode structure and state, and dividing the received reflected signal into a plurality of sub-signal segments, each signal segment corresponding to a different physical position of the flexible anode; Design a position mapping function to determine the specific location of the fault point and convert the high-dimensional feature space into interpretable physical preliminary position coordinates. The expression is: Where z(t) is the position feature vector of the flexible anode fault point at time t, W loc is the weight matrix of the position mapping layer, b loc is the bias vector of the position mapping layer, P(z(t)) represents the position coordinates of the fault point obtained by the position mapping function P based on the position feature vector z(t) at time point t, V is the propagation speed of the detection signal in the flexible anode, and Δt is the time difference from the emission to the reflection of the detection signal; Import the fault point location coordinates provided by P(z(t)) into the signal analysis system as the key area for analysis; Use FFT to extract the frequency characteristics of the signal, observe whether there are new frequency components and changes in the intensity of the original components, compare the extracted signal characteristics with the historical fault mode data, find similar characteristic patterns, and combine the position coordinates provided by P(z(t)) and the signal analysis results to generate the final fault point location.

2. The flexible anode fault detection method according to claim 1, characterized in that: The specific steps of real-time collection of the operating data of the flexible anode are as follows: Deploy various types of sensors at key locations of the flexible anode, including temperature sensors, current sensors, voltage sensors, and fiber Bragg grating sensors; Through the deployment of various types of sensors, the operation data of the flexible anode is collected in real time.

3. The flexible anode fault detection method according to claim 2, characterized in that: The specific steps of preprocessing the collected operation data are as follows: Wavelet transform is used for multi-scale analysis to decompose the signal into different frequency components, retaining useful information while removing high-frequency random noise; Fill missing values ​​using time series model predictions; Use Z-score standardization to convert the running data to the same magnitude; Use FFT to convert the time signal into a spectrum, analyze the frequency components of the signal, and extract the main frequency and frequency band energy characteristics; Each extracted main frequency and frequency band feature is taken as a dimension, and a multi-dimensional feature space is constructed by horizontal splicing.

4. The flexible anode fault detection method according to claim 3, characterized in that: Based on the preprocessed data, a fault prediction model is constructed to predict the fault of the flexible anode. The specific steps are as follows: Use long short-term memory network to build a fault prediction model; Divide the preprocessed dataset into training set, validation set and test set; Use the training set data to train the model, optimize the model weights through the back propagation algorithm, and minimize the loss function; Evaluate model performance on the validation set and adjust hyperparameters; Evaluate the generalization ability of the model on an independent test set; The preprocessed data set is input into the trained fault prediction model to predict the failure probability of the flexible anode. The expression is: y(t)=σ(W out ·S(D(t))+b out ); in, represents the probability of failure of the flexible anode at time t, W out is the weight matrix of the fully connected layer, b out is the bias vector of the fully connected layer, σ represents the sigmoid function, D(t) represents the preprocessed dataset at time point t, and S is a feature transformation function.

5. The flexible anode fault detection method according to claim 4, characterized in that: According to the prediction results, the flexible anode is identified for faults. The specific steps are as follows: Based on the analysis of fault records in historical data, a safety warning threshold T is defined, and the probability of flexible anode failure is compared. and the size of the safety warning threshold T to identify faults; when When , it is considered that there is no fault in the flexible anode; when When , the flexible anode is considered to be faulty.

6. The flexible anode fault detection method according to claim 5, characterized in that: The fault detection report includes the time and location of the fault and the systems and components involved; In-depth analysis of the root cause of the failure, providing detailed failure cause analysis; The report provides detailed handling measures, including emergency response steps, fault repair process and corrective measures taken, and also puts forward prevention and improvement suggestions.

7. A flexible anode fault detection system, based on the flexible anode fault detection method according to any one of claims 1 to 6, characterized in that: It includes data acquisition and preprocessing module, fault prediction module, fault identification module, fault point location module and fault detection report generation module; A data acquisition and preprocessing module is used to collect the operating data of the flexible anode in real time and preprocess the collected operating data; The fault prediction module builds a fault prediction model based on the preprocessed data to predict the fault of the flexible anode; A fault identification module, used for identifying the fault of the flexible anode according to the prediction result; A fault point location module is used to transmit a detection signal to the flexible anode based on the analysis result, and detect the fault point of the flexible anode by analyzing the reflection mode of the signal; The fault detection report generation module is used to generate a fault detection report by combining the fault point detection result of the flexible anode and the prediction result of the fault prediction model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the flexible anode fault detection method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the flexible anode fault detection method according to any one of claims 1 to 6 are implemented.

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