Wind driven generator fault intelligent detection system and method

Abstract

The invention discloses an intelligent fault detection system and method for a wind driven generator, and relates to the technical field of deep learning, and the method comprises the steps: collecting multi-dimensional data, and carrying out the preprocessing; extracting multi-dimensional features from the preprocessed multi-dimensional data, fusing the multi-dimensional features through a graph neural network and short-time Fourier transform, and generating a time-frequency domain signal; key features are extracted from the time-frequency domain signals, and fault features of the operation state of the wind driven generator are detected through a support vector machine model; based on the fault features, a fusion neural network model is constructed, fault prediction is generated, a diagnosis report is output, and a maintenance suggestion is provided; according to the method, the neural network model is fused, the convolutional neural network and the long-short-term memory network are combined, the spatial features and the time sequence data are extracted, and multi-dimensional characterization of the fault features of the wind driven generator is achieved.

Description

Technical Field

[0001] The present invention relates to the field of deep learning technology, and in particular to a wind turbine fault intelligent detection system and method. Background Art

[0002] In recent years, with the rapid development of wind power generation technology, the operating reliability and maintenance cost of wind turbines have become the focus of industry attention. Traditional fault detection methods mainly rely on regular manual inspections and data analysis of single sensors. These methods are not only time-consuming and labor-intensive, but also unable to accurately capture the fault characteristics of wind turbines in real time. With the improvement of sensor technology and data processing capabilities, multi-sensor data fusion technology has been widely used, which can comprehensively analyze multi-dimensional data such as temperature, vibration, and current, providing a more comprehensive perspective for fault detection. However, there are still great challenges in the effective fusion of multi-source data and its feature extraction in the time and frequency domains in the existing technology.

[0003] The existing wind turbine fault detection technology has the following main shortcomings: first, the data from a single sensor is easily affected by environmental noise and changes in operating conditions, making it difficult to ensure the accuracy of detection; second, traditional data processing and feature extraction methods often ignore the correlation between different data sources, resulting in insufficient information utilization; finally, most of the existing fault detection models lack real-time and intelligence, and are unable to quickly respond to and predict potential faults. Summary of the invention

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

[0005] Therefore, the present invention provides a wind turbine fault intelligent detection method to solve the problem of accurate prediction and real-time diagnosis of faults.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, the present invention provides an intelligent detection method for wind turbine faults, which includes collecting multidimensional data and performing preprocessing; extracting multidimensional features from the preprocessed multidimensional data, and fusing the multidimensional features through graph neural networks and short-time Fourier transforms to generate time-frequency domain signals; extracting key features from the time-frequency domain signals, and detecting the fault characteristics of the wind turbine operating status through a support vector machine model; based on the fault characteristics, constructing a fusion neural network model, generating fault predictions, and outputting a diagnosis report to make maintenance recommendations.

[0008] As a preferred solution of the intelligent detection method for wind turbine faults described in the present invention, the multi-dimensional data includes temperature data, vibration data and current data.

[0009] As a preferred solution of the intelligent detection method for wind turbine faults described in the present invention, the preprocessing includes denoising, filtering and outlier removal.

[0010] As a preferred solution of the wind turbine fault intelligent detection method described in the present invention, the multi-dimensional features include:

[0011] Extract average temperature, temperature change rate, and temperature peak value from temperature data as temperature features;

[0012] Extract vibration amplitude, vibration frequency, and vibration energy from vibration data as vibration features;

[0013] The average current, current fluctuation and current peak are extracted from the current data as current features.

[0014] As a preferred solution of the wind turbine fault intelligent detection method described in the present invention, wherein: the multi-dimensional features are fused through graph neural network and short-time Fourier transform to generate time-frequency domain signals, the specific steps are:

[0015] The temperature characteristics, vibration characteristics and current characteristics are used as nodes of the graph;

[0016] The node features in the graph are fused through the graph neural network to generate a comprehensive feature set, which is expressed as:

[0017]

[0018] Among them, hv (k) is the integrated feature set after fusion, h is the feature vector, k is the kth iteration in the graph neural network, v is the node, σ is the activation function, N(v) is the set function, is the feature vector of neighbor node u in the graph neural network in the k-1th iteration, where u is the neighbor node of node v;

[0019] Based on the fused comprehensive feature set, short-time Fourier transform is performed to generate time-frequency domain signals, which are expressed as:

[0020]

[0021] Among them, Z(t,ω) is the time-frequency domain signal, t is the time, ω is the angular frequency, A(θ) is the time domain signal, θ is the integral variable, w(θ-t) is the window function, e -jωθ is a complex exponential function, j is an imaginary unit, is the integral symbol, ∞ is positive infinity, and -∞ is negative infinity.

[0022] As a preferred solution of the wind turbine fault intelligent detection method described in the present invention, wherein: the key features are extracted and the fault features of the wind turbine operating state are detected by a support vector machine model, and the specific steps are:

[0023] Extract spectral features, time domain features and time-frequency features from time-frequency domain signals;

[0024] Through multi-scale analysis, the time-varying characteristics of different frequency components in the time-frequency domain signals are captured, and the peaks and abnormal changes in the time-frequency domain signals are identified;

[0025] The spectral features, time domain features, time-frequency features and time-varying features are reduced in dimension through principal component analysis;

[0026] Use local linear embedding to find the neighbors of data points in the reduced spectral features, time domain features, time-frequency features, and time-varying features, and determine the local neighborhood;

[0027] In the local neighborhood of each data point, the linear reconstruction weight is calculated to minimize the embedding reconstruction error;

[0028] Find low-dimensional embedding coordinates that minimize the embedding-reconstruction error and capture nonlinear structural features;

[0029] The spectrum features, time domain features, time-frequency features and time-varying features after principal component analysis and local linear embedding are input into multiple classifiers, and the classification results are fused by voting to generate a comprehensive feature vector;

[0030] Use the pre-trained support vector machine model to train and classify the comprehensive feature vector to identify the fault type and severity of the wind turbine;

[0031] Cross-validate the support vector machine model to evaluate performance;

[0032] Continuously optimize feature extraction and classification based on verification results;

[0033] The optimized support vector machine model is applied to real-time monitoring to detect fault characteristics of wind turbine operating status.

[0034] As a preferred solution of the wind turbine fault intelligent detection method described in the present invention, the steps of constructing a fusion neural network model, generating fault prediction, outputting a diagnosis report, and proposing maintenance suggestions are as follows:

[0035] Define the fusion neural network model input as fault features, and output as fault types and prediction results;

[0036] Extract spatial features from fault features through convolutional layers;

[0037] The adaptive pooling layer is used to reduce the dimension of the spatial features extracted by the convolution layer, and the pooling parameters are dynamically adjusted according to the input fault features;

[0038] Capture time series data in fault features through long short-term memory network layer to obtain long-term dependencies and dynamic features in time series data;

[0039] The spatial features and dynamic features are integrated to generate the comprehensive fault feature vector, which is expressed as:

[0040] P t =tanh(CNN(x t )+LSTM(x t-1 )+b);

[0041] Among them, P t is the fault comprehensive feature vector, tanh is the hyperbolic tangent activation function, CNN(x t ) is the output of the convolutional layer, x t is the input feature at time t, LSTM(x t-1 ) is the output of the long short-term memory network layer, x t-1 is the input feature at time t-1, b is the hyperbolic tangent activation function;

[0042] The comprehensive fault feature vector is mapped to a multi-dimensional space through a fully connected layer to generate a fault prediction;

[0043] The output layer outputs the fault type and prediction result based on the fault prediction;

[0044] Generate a fault diagnosis report with fault type, fault location, fault severity, timestamp and data chart based on the fault type and prediction results;

[0045] Through fault diagnosis reports, maintenance suggestions are automatically generated and maintenance measures are taken.

[0046] In the second aspect, the present invention provides an intelligent detection system for wind turbine faults, including an acquisition module, a fusion module, a detection module and a prediction module; the acquisition module is used to acquire multidimensional data and perform preprocessing; the fusion module is used to extract multidimensional features from the preprocessed multidimensional data, and fuse the multidimensional features through graph neural networks and short-time Fourier transforms to generate time-frequency domain signals; the detection module is used to extract key features of time-frequency domain signals based on the time-frequency domain signals, and detect fault features of the wind turbine operating status through a support vector machine model; the prediction module is used to construct a fusion neural network model based on the fault features, generate fault predictions, output a diagnosis report, and make maintenance recommendations.

[0047] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of a wind turbine fault intelligent detection method as described in the first aspect of the present invention is implemented.

[0048] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of a method for intelligent detection of wind turbine faults as described in the first aspect of the present invention is implemented.

[0049] The beneficial effects of the present invention are as follows: the present invention extracts spatial features and time series data by fusing a neural network model, combining a convolutional neural network and a long short-term memory network, and realizes a multi-dimensional characterization of wind turbine fault features. The pooling layer performs dimensionality reduction to improve computational efficiency. After fusing features, a comprehensive fault feature vector is generated and mapped to a multi-dimensional space through a fully connected layer to generate accurate fault predictions. Finally, the output layer generates a diagnostic report and maintenance recommendations based on the fault prediction, which not only improves the accuracy and real-time performance of fault detection, but also significantly improves the overall reliability and maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0051] Figure 1 This is a flow chart of a wind turbine fault intelligent detection method in Example 1;

[0052] Figure 2 This is a module diagram of a wind turbine fault intelligent detection system in Example 1. DETAILED DESCRIPTION

[0053] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0055] 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.

[0056] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a wind turbine fault intelligent detection method, comprising the following steps:

[0057] S1. Collect multidimensional data and perform preprocessing.

[0058] Furthermore, a variety of sensors are installed at key locations of wind turbines, including temperature sensors, vibration sensors, and current sensors;

[0059] These sensors collect temperature data, vibration data and data of wind turbines in real time during operation, which are transmitted to the data processing center through wireless communication networks;

[0060] Temperature data, monitoring temperature changes in key wind turbine components, which is important for detecting problems such as overheating and temperature anomalies;

[0061] Vibration data, which monitors the vibrations generated during wind turbine operation. Vibration data can help detect mechanical failures, wear and imbalance, etc.

[0062] Current data, monitoring current changes in wind turbines, which is important for detecting problems such as electrical faults, short circuits, and current overloads.

[0063] S1.2, preprocessing, specifically,

[0064] Apply low-pass or band-pass filters to eliminate high-frequency noise;

[0065] Use sliding average or median filtering to smooth the data and reduce the impact of random noise;

[0066] Perform Fourier transform on the time domain signal to convert it into frequency domain signal;

[0067] Apply filters in the frequency domain to remove unwanted frequency components, and then restore the signal to the time domain through inverse Fourier transform;

[0068] Calculate the mean and standard deviation of the data and detect outliers using the 3σ principle;

[0069] Detect outliers using algorithms such as Isolation Forest or Local Outlier Factor;

[0070] Remove the detected outliers and use interpolation methods (such as linear interpolation and spline interpolation) to fill the positions of the outliers;

[0071] Scale the data to the range [0, 1];

[0072] Transform the data into a standard normal distribution with a mean of 0 and a standard deviation of 1;

[0073] It should be noted that by installing a variety of sensors, the temperature, vibration and current data of the wind turbine are collected in real time and transmitted to the data processing center. The temperature data helps detect overheating and temperature anomalies, the vibration data is used to identify mechanical failures and wear, and the current data monitors electrical failures. The preprocessing steps include denoising, smoothing, outlier detection and interpolation, and the data is scaled and standardized. In this way, the accuracy and reliability of the input data are ensured through high-quality data processing.

[0074] S2. Extract multidimensional features from the preprocessed multidimensional data, fuse the multidimensional features through graph neural network and short-time Fourier transform to generate time-frequency domain signals.

[0075] Furthermore, the average temperature, temperature change rate, and temperature peak value are extracted from the temperature data as temperature features;

[0076] The average temperature reflects the overall temperature level of the wind turbine over a period of time. It can help detect whether there is overheating and prevent temperature-related failures.

[0077] The temperature change rate describes the speed at which the temperature changes over time. By monitoring the temperature change rate, abnormal temperature rise or fall can be detected in time, and possible faults can be warned in advance.

[0078] Temperature peak records the highest temperature during the monitoring period. Temperature peak can provide information about the operation status of the equipment under extreme conditions and help identify potential failures due to overheating.

[0079] Extract vibration amplitude, vibration frequency, and vibration energy from vibration data as vibration features;

[0080] The vibration amplitude indicates the intensity of the vibration. A larger vibration amplitude usually indicates looseness of the mechanical structure or abnormal wear of the components. It is an important indicator of mechanical failure.

[0081] Vibration frequency reflects the periodicity or frequency characteristics of vibration. Vibration frequency analysis helps to identify specific mechanical components or failure modes, such as bearing damage, gear wear, etc.

[0082] Vibration energy refers to the energy level of the vibration signal. By monitoring vibration energy, the health status of the equipment under different operating conditions can be identified and abnormal operating conditions can be discovered;

[0083] Extracting average current, current fluctuation and current peak value from current data as current features;

[0084] Average current indicates the average current consumed by the device during operation. Abnormal average current values ​​may indicate a malfunction of an electrical component, such as a short circuit or overload.

[0085] Current fluctuation describes the change of current over time. Significant current fluctuation may indicate electrical instability or interference and is an important indicator for fault warning.

[0086] Current peak records the maximum current value during the monitoring period. Current peak helps detect the response of the equipment when the load changes and identify possible electrical faults;

[0087] The temperature feature, the vibration feature and the current feature are taken as nodes of the graph, for example, the temperature feature is one node, the vibration feature is another node, and the current feature is the third node;

[0088] In a graph neural network, the edges between nodes represent the relationship between sensor data. If the data of two sensors are highly correlated in time (such as the change patterns of temperature and vibration data are similar), an edge is added between them.

[0089] The node features in the graph are fused through the graph neural network to generate a comprehensive feature set, which is expressed as:

[0090]

[0091] Among them, hv (k) is the fused comprehensive feature set, which represents the feature vector of node v in the kth iteration, such as a feature of the temperature sensor. h is the feature vector, which is used to represent the state and attributes of the node. In the graph neural network, the feature vector contains a set of values ​​about the node, which can describe some important information of the node. For example, for the temperature sensor node, the feature vector may include the average, maximum, and minimum values ​​of the temperature. k is the kth iteration in the graph neural network. v is a node, a data point in the graph, such as the feature node of the temperature sensor. σ is an activation function, such as ReLU, Sigmoid, or Tanh, which is used to introduce nonlinearity to the model. |N(v)| is the inverse of the number of neighbors of node v, which is used to calculate the average value. N(v) is a set function, which represents the set of all neighbor nodes of node v. This set contains all nodes that are directly connected to node v. For example, if node v represents a temperature sensor, then N(v) may contain vibration sensors and current sensors that are directly connected to the temperature sensor. is the feature vector of the neighbor node u in the graph neural network in the k-1th iteration. u is the neighbor node of node v, which is a specific neighbor node in N(v). u is an element in the set N(v), that is, a specific neighbor node. In the aggregation operation, we will traverse all neighbor nodes u in N(v) and use their feature vectors for calculation;

[0092] Through this formula, the feature of node v is the average of the features of its neighboring nodes and is processed by the activation function. This method can effectively aggregate data features from different sensors to obtain a fused feature representation.

[0093] Based on the fused comprehensive feature set, short-time Fourier transform is performed to generate time-frequency domain signals, which are expressed as:

[0094]

[0095] Among them, Z(t,ω) is the time-frequency domain signal, which represents the signal value at time t and frequency ω. This signal is the result of short-time Fourier transform processing, which combines the information of time and frequency. t is the time, which represents the center moment of the analysis window, that is, the specific time point we are interested in. ω is the angular frequency, which represents the frequency component of the signal. A(θ) is the time domain signal, which represents the original signal at time θ. θ is the integral variable, which represents the time of the original signal. During the integration process, it traverses the time range of the entire signal. w(θ-t) is the window function, which focuses on a small section of the signal near time t. Commonly used windows include rectangular windows, Hanning windows, etc. -jωθ is a complex exponential function, which represents the frequency component of Fourier transform, j is an imaginary unit, ω is the angular frequency, is the integral symbol, which represents the integral operation on time θ, ranging from negative infinity to positive infinity, ∞ is positive infinity, and -∞ is negative infinity;

[0096] This formula achieves efficient processing of wind turbine signals and accurate fault detection through time-frequency analysis, local characteristic analysis, enhanced signal recognition and real-time monitoring, thus improving operational reliability and maintenance efficiency.

[0097] It should be noted that by extracting features from temperature, vibration and current data, and fusing them using graph neural networks and short-time Fourier transforms, time-frequency domain signals are generated, and different types of features are integrated together to reflect the overall operating status of the wind turbine. The graph neural network fuses multi-sensor data to extract comprehensive features, while the short-time Fourier transform provides time-frequency analysis of the signal. This comprehensive method improves the accuracy and real-time performance of fault detection, can effectively identify potential faults and provide accurate fault diagnosis, and ultimately improves the operating reliability and maintenance efficiency of wind turbines.

[0098] S3. Extract key features from time-frequency domain signals and detect fault characteristics of wind turbine operating status through support vector machine model.

[0099] Furthermore, spectrum features, time domain features and time-frequency features are extracted from the time-frequency domain signals;

[0100] Spectral features extract the energy distribution within a specific frequency range from time-frequency domain signals, such as power spectral density (PSD);

[0101] Time domain features are to calculate the statistical features of time-frequency domain signals over time, such as mean, variance, peak, skewness, kurtosis, etc.

[0102] Time-frequency features are to extract features such as instantaneous frequency, instantaneous amplitude, and time-frequency energy distribution;

[0103] The power spectral density (PSD) in the spectrum feature calculates the spectrum energy distribution in each time period and identifies the energy peaks at specific frequencies. These peaks may correspond to specific fault modes.

[0104] The instantaneous frequency and instantaneous amplitude in the time-frequency characteristics are extracted using methods such as Hilbert-Huang transform (HHT), which can reflect the mutation and non-steady-state characteristics of the signal.

[0105] Through multi-scale analysis, the time-varying characteristics of different frequency components in the time-frequency domain signals are captured, and the peaks and abnormal changes in the time-frequency domain signals are identified;

[0106] The spectral features, time domain features, time-frequency features and time-varying features are reduced in dimension by principal component analysis to extract the main components of the features (including power spectrum density in spectral features, mean and variance in time domain features, instantaneous frequency and instantaneous amplitude in time-frequency features, and peaks and abnormal changes in time-varying features, etc.);

[0107] Use local linear embedding to find the neighbors of data points in the reduced spectral features, time domain features, time-frequency features, and time-varying features, and determine the local neighborhood;

[0108] In the local neighborhood of each data point, the linear reconstruction weight is calculated to minimize the embedding reconstruction error; the data point can be represented by a linear combination of its neighbors to minimize the reconstruction error. The expression is:

[0109]

[0110] Among them, ∈ represents the reconstruction error, which means the sum of the reconstruction errors of all data points. The reconstruction error measures the difference between the original feature vector and the reconstructed feature vector. i is the index variable of the data point, which is used to indicate the number of the data point. There are Q data points in total. The index i traverses each data point from 1 to Q. i is the original feature vector of the i-th data point, which contains important information describing the data point. is the weighted average feature vector of the neighbors of the i data point, which is calculated by linear reconstruction weights and represents the reconstructed feature vector obtained by weighting the feature vectors of the neighbors in the local neighborhood;

[0111] Find low-dimensional embedding coordinates that minimize the embedding reconstruction error, capture nonlinear structural features, and improve the representativeness and discrimination of features;

[0112] Keep the reconstruction weight unchanged, find the low-dimensional embedding coordinates that minimize the embedding error in the low-dimensional space, and obtain the new coordinates of each data point in the low-dimensional space;

[0113] By solving the eigenvalue problem, we find the low-dimensional embedding coordinates that minimize the embedding error and obtain a low-dimensional representation, that is, the new coordinates of each data point in the low-dimensional space. This step ensures that the features after dimensionality reduction still retain their important geometric and structural information in the new space.

[0114] The spectrum features, time domain features, time-frequency features and time-varying features after principal component analysis and local linear embedding are input into multiple classifiers (such as decision tree, random forest, extreme gradient boosting, etc.), and the classification results are fused by voting to generate a comprehensive feature vector;

[0115] Normalize the comprehensive feature vector to ensure that the feature values ​​are within the same scale range, which is convenient for subsequent feature analysis and fault detection;

[0116] Use the pre-trained support vector machine model to train and classify the comprehensive feature vector to identify the fault type and severity of the wind turbine;

[0117] Cross-validate the support vector machine model and evaluate the performance (such as accuracy, recall, F1 score);

[0118] Continuously optimize feature extraction and classification based on verification results to improve the accuracy of fault detection and prediction;

[0119] The optimized support vector machine model is applied to real-time monitoring to detect fault characteristics of wind turbine operation status;

[0120] When a fault is detected, an alarm signal is automatically issued to remind maintenance personnel to check and repair;

[0121] It should be noted that by extracting spectral features, time domain features and time-frequency features from time-frequency domain signals, and using multi-scale analysis, PCA and LLE methods for dimensionality reduction and processing, the nonlinear structural characteristics can be effectively captured and the data complexity can be reduced. Subsequently, the integrated classifier and support vector machine model are trained and classified to identify the type and severity of wind turbine faults. Through cross-validation and real-time monitoring, the fault detection accuracy and prediction reliability are improved. At the same time, an alarm signal is automatically issued to remind maintenance personnel to deal with the fault in time.

[0122] S4. Based on the fault characteristics, a fusion neural network model is constructed to generate fault predictions, output diagnostic reports, and make maintenance recommendations.

[0123] Furthermore, the input of the fusion neural network model is defined as the fault feature, and the output is the fault type and prediction result;

[0124] The spatial features in the fault features are extracted through the convolution layer, and the local patterns between the features are identified through the convolution operation;

[0125] By applying multiple convolution kernels (filters), the input data is convolved. Each convolution kernel slides on the input data, and the dot product between the convolution kernel and the local area of ​​the input data is calculated to extract local features.

[0126] The convolution operation generates feature maps that capture local patterns and features in the input data. For example, in fault detection, the convolution layer can identify abnormal peaks, periodic changes, and other features in the vibration signal.

[0127] The pooling layer is used to reduce the dimensionality of the spatial features extracted by the convolution layer, reducing the complexity of the data while retaining important information;

[0128] Usually includes maximum pooling or average pooling, by applying pooling operations on feature maps to reduce the dimension of features while retaining important information;

[0129] Select the maximum value in each local area to reduce the size of the feature map. For example, a 2x2 maximum pooling operation will output the maximum value among the four values.

[0130] Calculate the average value of each local area to reduce the size of the feature map;

[0131] The adaptive pooling layer is used to reduce the dimension of the spatial features extracted by the convolution layer. The pooling parameters are dynamically adjusted according to the input fault features to reduce data complexity while retaining important information, which helps prevent overfitting and improve the generalization ability of the model.

[0132] Adaptive pooling is an advanced pooling method that can dynamically adjust the size and stride of the pooling window according to the input features to adapt to feature maps of different sizes. Through this dynamic adjustment, adaptive pooling can retain more feature information while reducing the dimension and avoid information loss.

[0133] Specifically, the adaptive pooling layer receives the output feature map of the convolution layer and dynamically adjusts the pooling parameters (such as pooling window size, stride, etc.) according to the input fault features.

[0134] For example, if the input feature map size is large, adaptive pooling may choose a larger pooling window and stride to reduce the size more effectively. If the input feature map size is small, a smaller pooling window and stride may be chosen to retain more details.

[0135] Through adaptive pooling, the size of the feature map is reduced, thereby reducing data complexity; this helps to reduce computational costs and improve the operating efficiency of the model;

[0136] Adaptive pooling can effectively reduce noise and irrelevant information in input features, which is crucial to prevent the model from overfitting during training;

[0137] By reducing the size of feature maps, adaptive pooling reduces the number of model parameters and reduces the risk of overfitting;

[0138] Adaptive pooling dynamically adjusts pooling parameters to ensure that important features are retained during dimensionality reduction, thereby improving the generalization ability of the model;

[0139] Capture time series data in fault features through long short-term memory network layer, obtain long-term dependencies and dynamic features in time series data, and process time correlation in input features;

[0140] Long Short-Term Memory (LSTM) is a special recurrent neural network (RNN) that can capture and memorize long-term dependencies in time series data. LSTM controls the flow of information through input gates, forget gates, and output gates, solving the gradient vanishing and gradient exploding problems in traditional recurrent neural networks (RNN).

[0141] The LSTM layer processes the input time series data and captures the dynamic characteristics of the data over time through memory cells and gating mechanisms. For example, in wind turbine fault detection, the LSTM layer can track the long-term trend of vibration signals and identify fault modes with a longer duration.

[0142] The hidden states and cell states generated by the LSTM layer of the long short-term memory network contain long-term dependencies and dynamic features in the time series data, providing rich information for subsequent fault prediction;

[0143] The spatial features and dynamic features are integrated to generate the comprehensive fault feature vector, which is expressed as:

[0144] P t =tanh(CNN(x t )+LSTM(x t-1 )+b);

[0145] Among them, P t is the fault comprehensive feature vector, the hidden state vector at time t, tanh is the hyperbolic tangent activation function, which is used to introduce nonlinear characteristics, CNN(x t ) is the output of the convolutional layer, x t is the input feature at time t, LSTM(x t-1 ) is the output of the long short-term memory network layer, x t-1 is the input feature at time t-1, b is the hyperbolic tangent activation function, which is used to introduce nonlinear characteristics;

[0146] This formula combines the advantages of CNN and LSTM and introduces activation functions and bias terms to achieve a comprehensive and accurate characterization of complex signals of wind turbines, improving the effect of fault detection and the adaptability of the model.

[0147] The comprehensive fault feature vector is mapped to a multi-dimensional space through a fully connected layer to generate a fault prediction;

[0148] In this multidimensional space, the fully connected layer maps the input feature vector to the output feature space through weights and biases to generate the final fault prediction result;

[0149] The output layer outputs the fault type and prediction result based on the fault prediction;

[0150] Generate a fault diagnosis report with fault type, fault location, fault severity, timestamp and data chart based on the fault type and prediction results;

[0151] Fault type describes the specific fault type identified, such as bearing fault, gear fault, etc.

[0152] The fault location indicates the specific location where the fault occurred, such as which component or area of ​​the wind turbine had a problem;

[0153] Fault Severity assesses the severity of the fault, such as minor, moderate, or severe;

[0154] Evaluate the severity of the fault according to the fault severity level, and set the threshold:

[0155] Minor fault: h t < If h < h1, the fault is evaluated as minor. Minor faults usually do not have a significant impact on the normal operation of the equipment, but should be monitored;

[0156] Medium fault: h1 < h t < h2, the fault is evaluated as medium. Medium faults may affect the equipment performance and require regular inspections and preventive maintenance;

[0157] Severe fault: h t ≥ h2, the fault is evaluated as severe. Severe faults will significantly affect the equipment operation, may cause downtime or equipment damage, and immediate repair measures are required;

[0158] It should be noted that the threshold setting for evaluating the severity of the fault according to the fault severity level can be set according to the actual situation;

[0159] The timestamp records the specific time point when the fault is detected;

[0160] The data chart includes the chart display of the fault characteristic data, such as time domain and frequency domain charts, to visually display the fault characteristics;

[0161] Through the fault diagnosis report, automatically generate maintenance suggestions and make maintenance measures;

[0162] Specifically,

[0163] Suggested maintenance measures, such as replacing a certain component, lubricating, fastening, etc.;

[0164] Suggested repair time arrangement to ensure that the fault is handled at an early stage and prevent further damage;

[0165] Automatically send the generated fault diagnosis report and maintenance suggestions to the relevant maintenance personnel and management team; it can be distributed through email, text message or dedicated maintenance management;

[0166] All generated reports will be archived as the basis for subsequent analysis and optimization;

[0167] Real-time monitoring, continuously monitor the operating status of the wind turbine, and immediately trigger the alarm mechanism once a potential fault is detected;

[0168] When a fault is detected, automatically send an alarm signal to the maintenance team, and the notification methods include: text message, email and mobile application notification;

[0169] Send a fault alarm text message to the designated maintenance personnel;

[0170] Send an email with detailed fault information;

[0171] If the maintenance team uses a dedicated mobile app, send instant notifications;

[0172] It should be noted that by constructing a fusion neural network model, the spatial and temporal information of fault features can be extracted to generate accurate fault prediction results. Combined with the convolutional layer and the long short-term memory network layer, the model can identify abnormal patterns and long-term dependencies of wind turbines. The adaptive pooling and fully connected layers are used to improve the effectiveness and generalization of feature extraction and classification. Diagnostic reports and maintenance recommendations can help to quickly take maintenance measures, reduce the risk of equipment failure, and ensure operational stability and reliability. The automatic alarm and real-time monitoring mechanism further improve the response speed and efficiency of fault management.

[0173] The present embodiment also provides an intelligent fault detection system for wind turbines, including: an acquisition module, a fusion module, a detection module and a prediction module; the acquisition module is used to acquire multidimensional data and perform preprocessing; the fusion module is used to extract multidimensional features from the preprocessed multidimensional data, and fuse the multidimensional features through graph neural networks and short-time Fourier transforms to generate time-frequency domain signals; the detection module is used to extract key features of time-frequency domain signals based on the time-frequency domain signals, and detect fault features of the operating status of the wind turbine through a support vector machine model; the prediction module is used to construct a fusion neural network model based on the fault features, generate fault predictions, output a diagnosis report, and make maintenance recommendations.

[0174] This embodiment also provides a computer device, which is applicable to a method for intelligently detecting faults of wind turbines, and includes: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement a method for intelligently detecting faults of wind turbines as proposed in the above embodiment.

[0175] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. 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 a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0176] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a method for intelligent detection of wind turbine faults as proposed in the above embodiment is implemented; 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 (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0177] In summary, the present invention combines the convolutional neural network and the long short-term memory network through the fusion neural network model, extracts spatial features and time series data, realizes the multi-dimensional characterization of the fault characteristics of the wind turbine, and performs dimensionality reduction in the pooling layer to improve the computational efficiency. After the features are fused, a comprehensive fault feature vector is generated and mapped to the multi-dimensional space through the fully connected layer to generate accurate fault predictions. Finally, the output layer generates a diagnostic report and maintenance recommendations based on the fault prediction, which not only improves the accuracy and real-time performance of fault detection, but also significantly improves the overall reliability and maintenance efficiency.

[0178] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A wind turbine fault intelligent detection method, characterized in that: include, Collect multidimensional data and perform preprocessing; Extract multidimensional features from the preprocessed multidimensional data, fuse the multidimensional features through graph neural network and short-time Fourier transform to generate time-frequency domain signals; Extract key features from time-frequency domain signals and detect fault features of wind turbine operation status through support vector machine model; Based on the fault characteristics, a fusion neural network model is constructed to generate fault predictions, output diagnostic reports, and make maintenance recommendations.

2. A wind turbine fault intelligent detection method according to claim 1, characterized in that: The multi-dimensional data includes temperature data, vibration data and current data.

3. A wind turbine fault intelligent detection method according to claim 2, characterized in that: The preprocessing includes denoising, filtering and outlier removal.

4. A wind turbine fault intelligent detection method according to claim 3, characterized in that: Multidimensional features, including Extract average temperature, temperature change rate, and temperature peak value from temperature data as temperature features; Extract vibration amplitude, vibration frequency, and vibration energy from vibration data as vibration features; The average current, current fluctuation and current peak are extracted from the current data as current features.

5. A wind turbine fault intelligent detection method according to claim 4, characterized in that: The multi-dimensional features are fused through graph neural network and short-time Fourier transform to generate time-frequency domain signals. The specific steps are: The temperature characteristics, vibration characteristics and current characteristics are used as nodes of the graph; The node features in the graph are fused through the graph neural network to generate a comprehensive feature set, which is expressed as: Among them, hv (k) is the integrated feature set after fusion, h is the feature vector, k is the kth iteration in the graph neural network, v is the node, σ is the activation function, N(v) is the set function, is the feature vector of neighbor node u in the graph neural network in the k-1th iteration, where u is the neighbor node of node v; Based on the fused comprehensive feature set, short-time Fourier transform is performed to generate time-frequency domain signals, which are expressed as: Among them, Z(t,ω) is the time-frequency domain signal, t is the time, ω is the angular frequency, A(θ) is the time domain signal, θ is the integral variable, w(θ-t) is the window function, e -jωθ is a complex exponential function, j is an imaginary unit, is the integral symbol, ∞ is positive infinity, and -∞ is negative infinity.

6. A wind turbine fault intelligent detection method according to claim 5, characterized in that: The key features are extracted to detect the fault features of the wind turbine operating status through the support vector machine model. The specific steps are: Extract spectral features, time domain features and time-frequency features from time-frequency domain signals; Through multi-scale analysis, the time-varying characteristics of different frequency components in the time-frequency domain signals are captured, and the peaks and abnormal changes in the time-frequency domain signals are identified; The spectral features, time domain features, time-frequency features and time-varying features are reduced in dimension through principal component analysis; Use local linear embedding to find the neighbors of data points in the reduced spectral features, time domain features, time-frequency features, and time-varying features, and determine the local neighborhood; In the local neighborhood of each data point, the linear reconstruction weight is calculated to minimize the embedding reconstruction error; Find low-dimensional embedding coordinates that minimize the embedding-reconstruction error and capture nonlinear structural features; The spectrum features, time domain features, time-frequency features and time-varying features after principal component analysis and local linear embedding are input into multiple classifiers, and the classification results are fused by voting to generate a comprehensive feature vector; Use the pre-trained support vector machine model to train and classify the comprehensive feature vector to identify the fault type and severity of the wind turbine; Cross-validate the support vector machine model to evaluate performance; Continuously optimize feature extraction and classification based on verification results; The optimized support vector machine model is applied to real-time monitoring to detect fault characteristics of wind turbine operating status.

7. A wind turbine fault intelligent detection method according to claim 6, characterized in that: The fusion neural network model is constructed to generate fault predictions, output diagnostic reports, and make maintenance recommendations. The specific steps are: Define the fusion neural network model input as fault features, and output as fault types and prediction results; Extract spatial features from fault features through convolutional layers; The adaptive pooling layer is used to reduce the dimension of the spatial features extracted by the convolution layer, and the pooling parameters are dynamically adjusted according to the input fault features; Capture time series data in fault features through long short-term memory network layer to obtain long-term dependencies and dynamic features in time series data; The spatial features and dynamic features are integrated to generate the comprehensive fault feature vector, which is expressed as: P t =tanh(CNN(x t )+LSTM(x t-1 )+b); Among them, P t is the fault comprehensive feature vector, tanh is the hyperbolic tangent activation function, CNN(x t ) is the output of the convolutional layer, x t is the input feature at time t, LSTM(x t-1 ) is the output of the long short-term memory network layer, x t-1 is the input feature at time t-1, b is the hyperbolic tangent activation function; The comprehensive fault feature vector is mapped to a multi-dimensional space through a fully connected layer to generate a fault prediction; The output layer outputs the fault type and prediction result based on the fault prediction; Generate a fault diagnosis report with fault type, fault location, fault severity, timestamp and data chart based on the fault type and prediction results; Through fault diagnosis reports, maintenance suggestions are automatically generated and maintenance measures are taken.

8. An intelligent detection system for wind turbine faults, based on an intelligent detection method for wind turbine faults according to any one of claims 1 to 7, characterized in that: Including, acquisition module, fusion module, detection module and prediction module; The acquisition module is used to acquire multi-dimensional data and perform pre-processing; The fusion module is used to extract multidimensional features from the preprocessed multidimensional data, and fuse the multidimensional features through graph neural network and short-time Fourier transform to generate time-frequency domain signals; The detection module is used to extract key features of the time-frequency domain signals according to the time-frequency domain signals, and detect fault features of the wind turbine operating state through a support vector machine model; The prediction module is used to build a fusion neural network model based on fault characteristics, generate fault predictions, output a diagnosis report, and make maintenance recommendations.

9. 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 wind turbine fault intelligent detection method according to any one of claims 1 to 7 are implemented.

10. 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 a wind turbine fault intelligent detection method according to any one of claims 1 to 7 are implemented.

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