Wind Turbine Gearbox Fault Early Warning System Based on Time Series Analysis

Abstract

The present invention relates to the technical field of parameter data processing of a wind turbine gearbox, and particularly relates to a fault warning system for a wind turbine gearbox based on time series analysis. The system includes: a training matrix construction module for constructing a training matrix; an importance calculation module for calculating the importance of parameter data at a moment within a set step length based on the characteristics of the parameter data at each moment in their respective original sequences; a state irrelevance calculation module for calculating the state irrelevance of the parameter data at a moment based on the differences between the parameter data at a moment and those at other moments within a set step length and the differences between sampling moments; a data screening module for screening and constructing a process memory matrix based on the data retention degree of the parameter data; and a fault warning module for predicting the parameter data at the next moment, obtaining a warning threshold and giving a warning. This application optimizes the construction of the process memory matrix and improves the accuracy of gearbox warning.

Description

Technical Field

[0001] The present invention relates to the technical field of parameter data processing of a wind turbine gearbox, and particularly to a fault warning system for a wind turbine gearbox based on time series analysis. Background Art

[0002] A wind turbine, i.e., a wind power generation set, is a system that converts wind energy into electrical energy, and is mostly installed at windward places such as mountains, wildernesses, and beaches. A wind power gearbox is an important mechanical component in a wind turbine, and its main function is to transmit the power generated by the wind wheel under the action of wind force to the generator and enable it to obtain the corresponding rotational speed. Since the installation environment of the wind turbine is relatively harsh, and the gearbox is installed in a narrow space at the top of the tower, once a fault occurs, it is very difficult to repair. Therefore, it is necessary to perform fault warning on the wind turbine gearbox.

[0003] Traditionally, various physical quantities such as vibration, temperature, rotational speed, etc. of the running state of the gearbox are collected through sensors, and thresholds are set for these data for warning. However, the thresholds of this warning method are fixed and lack a comprehensive analysis of the running state of the gearbox. The NSET (Nonlinear State Estimation) prediction model compares the current running data with the generated historical running states by synthesizing the time series data of multiple parameters, calculates and compares the similarities between multiple state variables, so as to perform fault warning. This model has the characteristics of considering multi-dimensional data for early warning and being fast and reliable. Therefore, when using the NSET warning model to predict the fault state of the gearbox, the construction of the process memory matrix in the NEST prediction model is particularly important. Traditionally, the fixed-step sampling method is used to perform equidistant sampling from the training matrix, and the data that meet the conditions are added to the process matrix. However, due to the influence of the wind speed change in the natural environment, the running state of the wind turbine fluctuates continuously. This method of constructing the process matrix has a large randomness and is prone to missing some key running state data. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a fault warning system for a wind turbine gearbox based on time series analysis, and the specific technical solution adopted is as follows:

[0005] An embodiment of the present invention provides a fault warning system for a wind turbine gearbox based on time series analysis, and the system includes:

[0006] A training matrix construction module, configured to obtain the historical parameter data of the wind turbine gearbox and construct a training matrix;

[0007] An importance calculation module, configured to sample from the training matrix using a set step size, and calculate the importance of the parameter data at a moment within the set step size according to the adjacent parameter data of the parameter data at each moment in their respective original sequences;

[0008] A state non - correlation calculation module, which is used to calculate the state non - correlation of the parameter data at a moment according to the differences between the parameter data at one moment and other moments within a set step length and the differences between sampling moments;

[0009] A data screening module, which is used to obtain the data retention degree of the parameter data at a moment according to the importance degree and state non - correlation of the parameter data at a moment within a set step length; and use the data retention degree to screen the parameter data at each moment within each set step length and form a process memory matrix;

[0010] A fault warning module, which is used to predict the parameter data at the next moment based on the process memory matrix; obtain a warning threshold, and perform warning on the operating state of the gearbox of the wind turbine at the next moment according to the warning threshold.

[0011] Preferably, obtain the historical parameter data of the gearbox of the wind turbine and construct a training matrix, including:

[0012] Pre - process the historical parameter data of the gearbox of the wind turbine, and construct an observation matrix by using the pre - processed historical parameter data; construct a training matrix based on the observation matrix, and the parameter data includes gearbox temperature, converter temperature, engine active power, and vibration data.

[0013] Preferably, calculate the importance degree of the parameter data at a moment according to the parameter data adjacent to the parameter data at a moment within a set step length in their respective original sequences, including:

[0014] Obtain the mean value of a preset number of parameter data adjacent to the left of a parameter data at a moment in its own corresponding original sequence within a set step length, denoted as the left mean value; obtain the mean value of a preset number of parameter data adjacent to the right of a parameter data at a moment in its own corresponding original sequence within a set step length, denoted as the right mean value; respectively obtain the absolute values of the differences between the parameter data and the corresponding left mean value and right mean value, and take the maximum value among them as the left - right maximum difference of the parameter data. Average and normalize the left - right maximum differences of the parameter data at a moment to obtain the first distribution feature of the parameter data at this moment;

[0015] Obtain the number of normal parameter data among a preset number of parameter data adjacent to the left and a preset number of parameter data adjacent to the right of the parameter data in its own corresponding original sequence; average and normalize the number of normal parameter data corresponding to each parameter data at a moment to obtain the second distribution feature of the parameter data at this moment;

[0016] Average the first distribution feature and the second distribution feature to obtain the importance degree of the parameter data at this moment.

[0017] Preferably, calculating the state irrelevance of the parameter data at a moment according to the differences between the parameter data at one moment and those at other moments within a set step length and the differences between sampling moments includes:

[0018] Calculating the Euclidean distance between the parameter data at one moment and the parameter data at other moments within a set step length and taking the average to obtain the distance difference of the parameter data at this moment; calculating the absolute value of the difference between the sampling moment of the parameter data at one moment and the sampling moments of the parameter data at other moments within a set step length and taking the average to obtain the timing difference of the parameter data at this moment; taking the average of the normalized distance difference and the normalized timing difference to obtain the state irrelevance of the data difference at this moment.

[0019] Preferably, obtaining the data retention degree of the parameter data at a moment according to the importance degree and state irrelevance of the parameter data at a moment within a set step length includes:

[0020] Performing weighted summation on the importance degree and state irrelevance of the parameter data at a moment within a set step length to obtain the retention degree of the parameter data at this moment.

[0021] Preferably, screening the parameter data at each moment within each set step length by using the data retention degree and forming a process memory matrix, including:

[0022] When the retention degree of the parameter data at a moment in a set step length in the gearbox training matrix is greater than or equal to the retention threshold, retaining the parameter data at this moment and adding the parameter data at this moment to the process memory matrix to obtain the process memory matrix.

[0023] Preferably, predicting the parameter data at the next moment based on the process memory matrix includes:

[0024] Using the NSET prediction model to predict the parameter data at the next moment based on the vector composed of the process memory matrix and the parameter data at the current moment.

[0025] Preferably, obtaining the warning threshold includes:

[0026] Obtaining the parameter data at each moment in the training matrix and making predictions to obtain the predicted parameter data at each moment; subtracting the parameter data at a moment from the predicted parameter data to obtain the residual sequence corresponding to this moment; obtaining the mean value of the residuals of the residual sequence at a moment and the root mean square error , obtaining the corresponding lower limit value and upper limit value at this moment, the lower limit value is , the upper limit value is , then taking the mean values of the lower limit values and upper limit values corresponding to each moment respectively to obtain the average lower limit value and the average upper limit value, and the range between the average lower limit value and the average upper limit value is the warning threshold.

[0027] Preferably, the operating state of the gearbox of the wind turbine at the next moment is warned according to the warning threshold, including:

[0028] Subtract the true parameter data at the next moment from the predicted parameter data to obtain the residual sequence corresponding to the next moment; obtain the lower limit value and the upper limit value corresponding to the next moment according to the residual sequence at the next moment, compare them with the warning threshold, and give a warning if the warning threshold is exceeded.

[0029] The embodiments of the present invention have at least the following beneficial effects: The present application first constructs a training matrix based on the historical parameter data of the gearbox of the wind turbine. Further, samples are taken from the training matrix using a set step size, and then the characteristics of the parameter data at each moment within each set step size in the original data sequence are analyzed, so as to obtain the importance of the parameter data at each moment within each set step size. Then, the correlation between the parameter data at each moment within the set step size and the parameter data at other moments is analyzed, so as to obtain the state non-correlation corresponding to the parameter data at each moment. Combining the two takes into account the importance and uniqueness of the parameter data, and obtains the retention degree of the parameter data at each moment within the set step size, so that the selected parameter data has a low redundancy degree and a high integrity, achieving the purpose of optimizing the construction of the process memory matrix, making the parameter data vectors at each moment in the obtained process memory matrix have less redundancy and more obvious features, making the prediction more accurate, and improving the reliability and accuracy of the fault warning. Description of the Drawings

[0030] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. 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.

[0031] Figure 1 It is a system block diagram of a fault warning system for a wind turbine gearbox based on time series analysis provided by an embodiment of the present invention. Detailed Embodiments

[0032] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following combines the drawings and preferred embodiments to describe in detail the specific embodiments, structures, features and effects of a fault warning system for a wind turbine gearbox based on time series analysis proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs.

[0034] The following specifically describes the specific solution of a fault warning system for a wind turbine gearbox based on time series analysis provided by the present invention with reference to the accompanying drawings.

[0035] Embodiment:

[0036] The main application scenario of the present invention is as follows: Traditionally, the fault monitoring and warning of wind turbine gearboxes mainly collect various parameter data during the operation of the gearbox through sensors, such as vibration, temperature, speed data, etc., and then analyze each parameter data and set thresholds. When the fault risk reaches the preset threshold, the system triggers an alarm. The present invention optimizes the NEST prediction model and then monitors the faults of the wind turbine gearbox.

[0037] Please refer to Figure 1 , which shows a system block diagram of a fault warning system for a wind turbine gearbox based on time series analysis provided by an embodiment of the present invention. The system includes the following modules:

[0038] A training matrix construction module, configured to obtain historical parameter data of a wind turbine gearbox and construct a training matrix.

[0039] Since the NSET prediction model is required to predict the operating state of the gearbox, various types of historical data during the operation of the gearbox need to be obtained. These data are all time series data, including various types of data such as gearbox temperature, converter temperature, engine active power, and vibration data. These data are denoted as parameter data, and the historical parameter data during the operation of the wind turbine gearbox is preprocessed, including denoising, missing value filling, and outlier removal.

[0040] An observation matrix P is constructed using the preprocessed historical parameter data. The observation matrix is specifically:

[0041] ,

[0042] where b is the number of data in different time periods during monitoring, n represents the number of types of parameter data collected at one moment, that is, the number of parameter data collected at one moment. One column in the observation matrix is various parameters at one moment, which is called an observation vector.

[0043] Further, a training matrix K is constructed according to the constructed observation matrix. The training matrix K is specifically:

[0044] ,

[0045] The training matrix K is composed of k observation vectors selected from the observation matrix P. Each observation vector is generated when the gearbox is in a normal working state. That is, the above training matrix K is the matrix obtained by removing all parameter data in abnormal states from the observation matrix P. n is the number of selected parameter data, and n is 4 in this application.

[0046] The importance calculation module is used to sample from the training matrix using a set step size, and calculate the importance of the parameter data at a certain moment according to the adjacent parameter data of each parameter data at a moment within the set step size in their respective original sequences.

[0047] When predicting the operating state of the gearbox through the traditional NSET prediction model, the construction of the process memory matrix is to select observation vectors that meet the threshold conditions in each step size interval of the training matrix K at a fixed step size, and then add the observation vectors that meet the conditions to the matrix. Affected by the change of wind speed in the natural environment, the operating state of the wind turbine continuously fluctuates. This method cannot accurately reflect the normal operating characteristics of the wind turbine. Therefore, it is necessary to analyze the data characteristics of the parameters at each moment within the fixed step size interval, and then obtain the retention degree of the parameter data at each moment within the step size interval; the greater the retention degree of the parameter data at a certain moment within the step size interval, it means that the parameter data at this moment is more important and is data in a relatively special normal state. At this time, add the parameter data at this moment to the process memory matrix, and then give an early warning of the state of the wind turbine gearbox at the next moment.

[0048] The higher the importance of the parameter data at a certain moment within each step size in the training matrix of the gearbox, the more important the parameter data at this moment is for the state monitoring of the wind turbine gearbox. That is, the data at this moment has a greater impact on the model prediction result. At this time, the parameter data at this moment should be retained. Therefore, analyze the characteristics of each parameter data at each moment in their respective original sequences to obtain the importance of the parameter data at each moment.

[0049] To eliminate the influence of dimensions, first normalize the obtained training matrix, and denote the normalized training matrix as , assuming that the original fixed-step sampling method sets the step size to h when sampling the training matrix .

[0050] Sample from the training matrix using a set step size, and calculate the importance of the parameter data at a certain moment according to the adjacent parameter data of each parameter data at a moment within the set step size in their respective original sequences.

[0051] Specifically, calculate the mean of a preset number of parameter data adjacent to the left of a parameter data at a certain moment within a set step length in its corresponding original sequence, denoted as the left mean; calculate the mean of a preset number of parameter data adjacent to the right of a parameter data at a certain moment within a set step length in its corresponding original sequence, denoted as the right mean; calculate the absolute values of the differences between the parameter data and the corresponding left mean and right mean respectively, and take the maximum value as the left-right maximum difference of the parameter data. Average and normalize the left-right maximum differences of the parameter data at a certain moment to obtain the first distribution feature of the parameter data at this moment; obtain the number of normal parameter data among the preset number of parameter data adjacent to the left and the preset number of parameter data adjacent to the right of the parameter data in its corresponding original sequence; average and normalize the number of normal parameter data corresponding to each parameter data at a certain moment to obtain the second distribution feature of the parameter data at this moment; average the first distribution feature and the second distribution feature to obtain the importance degree of the parameter data at this moment.

[0052] The calculation model for the degree of parameter data at a certain moment within a set step length is specifically as follows:

[0053] Among them, represents the importance degree of the parameter data at the j-th moment in the i-th set step length, represents the normalization operation, M represents the number of parameter data in a moment, m represents the m-th parameter in a moment, is the parameter data adjacent to one side of the m-th parameter data at the j-th moment in the i-th set step length in its own original data sequence, k represents the preset number, and the reference value is 10. The implementer can adjust it according to the amount of data; The absolute value of the difference between the m-th parameter data at the j-th moment in the i-th set step length and its corresponding left mean, represents the absolute value of the difference between the m-th parameter data at the j-th moment in the i-th set step length and its corresponding right mean, represents taking the maximum value of the two; represents the first distribution feature. The larger this value is, the more the parameter data at the j-th moment in the i-th set step length shows turning points in the original data sequence. Also, since the training data is the operation data of the gearbox in the normal state selected from the original observation matrix, when the parameter data at this moment has the feature of turning points, it means that the importance degree of the parameter data at this moment is greater; is the number of the parameter data adjacent to the left and right of the m-th parameter data at the j-th moment in the i-th set step length in its own original data sequence. The number of parameter data adjacent to both sides is equal, It represents the number of normal parameter data among the preset number of parameter data adjacent to the left and the preset number of parameter data adjacent to the right of the parameter data in its corresponding original sequence. The larger this number is, the more stable the overall data state of the neighborhood where the m-th parameter data is located in the original data sequence. At this time, the corresponding importance degree is greater. It represents the second distribution feature. The larger this value is, the higher the importance degree of the parameter data at a certain moment. Thus, the importance degree of the parameter data at each moment within each step size can be calculated and obtained.

[0054] The state non-correlation calculation module is used to calculate the state non-correlation of the parameter data at a certain moment according to the difference between the parameter data at a certain moment and the parameter data at other moments within the set step size and the difference between the sampling moments.

[0055] The stronger the state non-correlation between the parameter data at a certain moment within the set step size and the remaining data means the stronger the uniqueness of the parameter data at this moment, that is, the parameter data at this moment is the normal operation data obtained by the gearbox under specific conditions. The parameter data at this moment reflects the operating state of the gearbox under characteristic conditions. At this time, the data at this moment should be retained; Therefore, the retention degree of the parameter data at the current moment is obtained by comprehensively considering the importance degree of the parameter data at each moment within each step size and its state non-correlation with the remaining data.

[0056] Calculating the state non-correlation of the parameter data at a certain moment according to the difference between the parameter data at a certain moment and the parameter data at other moments within the set step size and the difference between the sampling moments includes:

[0057] Calculating the Euclidean distance between the parameter data at a certain moment and the parameter data at other moments within the set step size and taking the average to obtain the distance difference of the parameter data at this moment; calculating the absolute value of the difference between the sampling moment of the parameter data at a certain moment and the sampling moments of the parameter data at other moments within the set step size and taking the average to obtain the time series difference of the parameter data at this moment; taking the average of the normalized distance difference and the normalized time series difference to obtain the state non-correlation of the data difference at this moment.

[0058] The calculation model of the state non-correlation between the parameter data at the j-th moment in the i-th set step size in the gearbox training matrix and the remaining data is:

[0059] ,

[0060] Among them, represents the state non-correlation of the parameter data at the j-th moment in the i-th set step size. I represents the number of data entries within a set step size, that is, how many moments of data are there within a set step size. represents the Euclidean distance between the parameter data at the j-th moment and the parameter data at the k-th moment within the i-th step length, represents the Euclidean distance operator. The Euclidean distance has an intuitive physical meaning and is a basic operator for measuring the similarity of two vectors. When calculating the Euclidean distance, the parameter data at one moment can be regarded as a vector; represents the mean value of the sum of the Euclidean distances between the parameter data at the j-th moment within the i-th step length in the gearbox training matrix and the parameter data at the remaining moments, that is, the distance difference. The greater this distance difference, the greater the difference between the parameter data at the two moments, that is, the greater the non-correlation;

[0061] and represent the sampling moments of the parameter data at the j-th moment and the k-th moment within the i-th set step length. The sampling moment is also the timestamp of the parameter data at the j-th moment and the k-th moment on their respective original sequences, represents the absolute value of the difference between the two sampling moments, represents the mean value of the absolute values of the differences between the sampling moment of the parameter data at the j-th moment and the sampling moments corresponding to the parameter data at other moments within the set step length, that is, the timing difference at the j-th moment. The larger this value, the greater the time span between the parameter data at the j-th moment within the i-th step length on the original data sequence and the parameter data at the remaining moments on the original data sequence. At this time, the possibility of non-correlation between the parameter data at the j-th moment and the parameter data at the remaining moments is greater.

[0062] Thus, the state non-correlation of the parameter data at each moment within each set step length can be obtained.

[0063] The data screening module is used to obtain the data retention degree of the parameter data at a moment according to the importance degree and state non-correlation of the parameter data at a moment within the set step length; use the data retention degree to screen the parameter data at each moment within each set step length and form a process memory matrix.

[0064] After obtaining the importance degree and state non-correlation of the parameter data at a moment within the set step length, it is necessary to comprehensively consider the two, and then obtain the importance degree of the parameter data at this moment. Specifically, the importance degree and state non-correlation of the parameter data at a moment within the set step length are weighted and summed to obtain the retention degree of the parameter data at this moment.

[0065] The calculation formula is:

[0066]

[0067] Among them, The retention degree of the parameter data at the j-th moment within the i-th set step length in the gearbox training matrix The importance degree of the parameter data at the j-th moment within the i-th set step length in the gearbox training matrix The state irrelevance of the parameter data at the j-th moment within the i-th set step length in the gearbox training matrix. Since the state irrelevance is more important for the establishment of the subsequent prediction model in terms of importance degree and state irrelevance, so let , , and the implementer can adjust the weight

[0068] The greater the retention degree of a certain parameter data in the gearbox training matrix, it means that the parameter data at this moment is more important and has a lower similarity with the remaining parameter data. At this time, this parameter data, as a relatively unique existence, can well reflect the state of the data to be predicted

[0069] Regarding the retention degree of the parameter data at each moment within each set step length obtained, a lower retention degree means that the parameter data at this moment has a stronger redundancy. At this time, the parameter data at this moment should not be selected for the construction of the process memory matrix; a higher retention degree means that the parameter data at this moment has a stronger importance and a greater irrelevance with the remaining data. At this time, the parameter data at this moment should be selected for the construction of the process matrix. Therefore, this solution stipulates that when the retention degree of the parameter data at a moment within a set step length in the gearbox training matrix is greater than or equal to the retention threshold, the parameter data at this moment is retained, and the parameter data at this moment is added to the process memory matrix, thereby obtaining the process memory matrix D. The reference value of the retention threshold is 0.65, and the implementer can adjust it according to the actual situation

[0070] A fault warning module, used to predict the parameter data at the next moment based on the process memory matrix; obtain a warning threshold, and warn about the operating state of the gearbox of the wind turbine at the next moment according to the warning threshold

[0071] After obtaining the process memory matrix, according to the existing NSET prediction model and the vector composed of the parameter data at the current moment, the vector composed of the predicted parameter data at the next moment can be obtained, that is, the parameter data at the next moment. The specific prediction model is

[0072] ,

[0073] Among them, The vector composed of the predicted parameter data of the gearbox at the next moment Is the process memory matrix constructed in this application Is the vector composed of the parameter data of the gearbox at the current moment is the Euclidean distance operator for two vectors. Thus, the NSET prediction model can be used to predict the parameter data at the next moment based on the vectors formed by the process memory matrix and the parameter data at the current moment.

[0074] Furthermore, the residual is used for early warning analysis. Specifically, the parameter data at each moment in the training matrix is obtained and predicted to obtain the predicted parameter data at each moment; the parameter data at a moment is subtracted from the predicted parameter data at that moment to obtain the residual sequence corresponding to that moment; the mean value of the residual sequence at a moment is calculated. and the root mean square error , to obtain the corresponding lower limit value and upper limit value at that moment. The lower limit value is , and the upper limit value is . Then, the mean values of the lower limit value and upper limit value corresponding to each moment are calculated respectively to obtain the average lower limit value and average upper limit value. The range between the average lower limit value and average upper limit value is the early warning threshold. It should be noted that the early warning threshold can be continuously updated as the historical data increases to ensure real-time adaptation to environmental changes. Specifically, a sliding window is established and slides with a fixed step size to obtain the parameter data and predicted parameter data at each moment when the gearbox is in normal operation within the sliding window, and then the early warning threshold is updated. The parameter data at each moment in the training matrix is the parameter data in the normal state.

[0075] Since it is necessary to determine whether the residual sequence at the next moment exceeds the early warning threshold, and the residual sequence is a vector and difficult to compare with a single threshold, it is necessary to calculate the residual sequence using the calculation method of the early warning threshold and compare the calculation result with the threshold to conduct a fault early warning.

[0076] Based on the early warning threshold, the operating state of the gearbox of the wind turbine at the next moment is warned. Specifically, the real parameter data at the next moment is subtracted from the predicted parameter data at the next moment to obtain the residual sequence corresponding to the next moment; the lower limit value and upper limit value corresponding to the next moment are obtained according to the residual sequence at the next moment and compared with the early warning threshold. If it exceeds the early warning threshold, a warning is issued.

[0077] In summary, according to the above method, the prediction result is judged for abnormality to conduct fault early warning. In this application, the NSET prediction model is used to predict the state of the wind turbine gearbox. In the process of constructing the prediction model, the key step of constructing the process memory matrix is optimized, which makes up for the disadvantages of the traditional method of sampling and screening the gearbox training data matrix based on a fixed step size to obtain the process memory matrix. When constructing the process memory matrix, it is affected by the change of wind speed in the natural environment, and the operating state of the wind turbine fluctuates continuously. There is a large randomness in the construction process memory matrix, which is likely to cause the loss of some key operating state data. In this application, the process memory matrix is constructed according to the own characteristics of the parameter data at each moment, so that the parameter data vectors at each moment in the obtained process matrix have less redundancy and more obvious characteristics, and thus the prediction is more accurate, increasing the reliability and accuracy of the fault early warning.

[0078] It should be noted that the above order of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. And the specific embodiments of this specification have been described above. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0079] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

[0080] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A wind turbine gearbox fault warning system based on time series analysis, characterized in that, The system includes: A training matrix construction module, configured to obtain historical parameter data of a wind turbine gearbox and construct a training matrix; An importance calculation module, configured to sample from the training matrix using a set step size, and calculate the importance of the parameter data at a certain moment according to the parameter data adjacent to the parameter data at that moment in their respective original sequences within the set step size; A state irrelevance calculation module, configured to calculate the state irrelevance of the parameter data at a certain moment according to the difference between the parameter data at a certain moment and the parameter data at other moments within the set step size and the difference between the sampling moments; A data screening module, configured to obtain the data retention degree of the parameter data at a certain moment according to the importance and state irrelevance of the parameter data at a certain moment within the set step size; use the data retention degree to screen the parameter data at each moment within each set step size and form a process memory matrix; A fault warning module, configured to predict the parameter data at the next moment based on the process memory matrix; obtain a warning threshold, and perform a warning on the operating state of the gearbox of the wind turbine at the next moment according to the warning threshold; The calculating the importance of the parameter data at a certain moment according to the parameter data adjacent to the parameter data at that moment in their respective original sequences within the set step size includes: Calculating the mean value of a preset number of parameter data adjacent to the left of a parameter data at a certain moment in its corresponding original sequence within the set step size, denoted as the left mean value; calculating the mean value of a preset number of parameter data adjacent to the right of a parameter data at a certain moment in its corresponding original sequence within the set step size, denoted as the right mean value; respectively calculating the absolute value of the difference between the parameter data and the corresponding left mean value and right mean value, taking the maximum value among them as the left and right maximum difference of the parameter data, averaging and normalizing the left and right maximum differences of the parameter data at a certain moment, to obtain the first distribution feature of the parameter data at that moment; Obtaining the number of normal parameter data among a preset number of parameter data adjacent to the left and a preset number of parameter data adjacent to the right of the parameter data in its corresponding original sequence; averaging and normalizing the number of normal parameter data corresponding to each parameter data at a certain moment, to obtain the second distribution feature of the parameter data at that moment; Averaging the first distribution feature and the second distribution feature to obtain the importance of the parameter data at that moment; The calculating the state irrelevance of the parameter data at a certain moment according to the difference between the parameter data at a certain moment and the parameter data at other moments within the set step size and the difference between the sampling moments includes: Calculating the Euclidean distance between the parameter data at a certain moment and the parameter data at other moments within the set step size and averaging to obtain the distance difference of the parameter data at that moment; calculating the absolute value of the difference between the sampling moment of the parameter data at a certain moment and the sampling moments of the parameter data at other moments within the set step size and averaging to obtain the timing difference of the parameter data at that moment; averaging the normalized distance difference and the normalized timing difference to obtain the state irrelevance of the data difference at that moment.

2. The fault warning system for a wind turbine gearbox based on timing analysis according to claim 1, wherein, The obtaining the historical parameter data of the wind turbine gearbox and constructing a training matrix includes: Preprocess the historical parameter data of the wind turbine gearbox, and construct an observation matrix using the preprocessed historical parameter data; construct a training matrix based on the observation matrix, where the parameter data includes gearbox temperature, converter temperature, engine active power, and vibration data.

3. The fault warning system for a wind turbine gearbox based on time series analysis according to claim 1, characterized in that, Obtaining the data retention degree of the parameter data at a certain moment according to the importance degree and state irrelevance of the parameter data at a certain moment within a set step length, including: Weighted sum the importance degree and state irrelevance of the parameter data at a certain moment within a set step length to obtain the retention degree of the parameter data at that moment.

4. The fault warning system for a wind turbine gearbox based on time series analysis according to claim 1, wherein Using the data retention degree to screen the parameter data at each moment within each set step length and form a process memory matrix, including: When the retention degree of the parameter data at a certain moment in a set step length in the gearbox training matrix is greater than or equal to the retention threshold, retain the parameter data at that moment, and add the parameter data at that moment to the process memory matrix to obtain the process memory matrix.

5. The fault warning system for a wind turbine gearbox based on timing analysis according to claim 1, wherein Predicting the parameter data at the next moment based on the process memory matrix, including: Using the NSET prediction model to predict the parameter data at the next moment based on the vector composed of the process memory matrix and the parameter data at the current moment.

6. The fault warning system for a wind turbine gearbox based on time series analysis according to claim 1, wherein, Obtaining the warning threshold, including: Obtain the parameter data at each moment in the training matrix, and perform prediction to obtain the predicted parameter data at each moment; subtract the parameter data at one moment from the predicted parameter data to obtain the residual sequence corresponding to that moment; calculate the mean residual of the residual sequence at one moment and the root mean square error , to obtain the corresponding lower limit value and upper limit value at that moment. The lower limit value is , and the upper limit value is . Then, calculate the mean of the lower limit value and upper limit value corresponding to each moment respectively to obtain the average lower limit value and average upper limit value. The range between the average lower limit value and average upper limit value is the warning threshold.

7. The fault warning system for a wind turbine gearbox based on timing analysis according to claim 1, characterized in that Warning the operating state of the gearbox of the wind turbine at the next moment according to the warning threshold, including: Subtract the predicted parameter data from the actual parameter data at the next moment to obtain the corresponding residual sequence at the next moment; obtain the corresponding lower limit value and upper limit value at the next moment according to the residual sequence at the next moment, and compare with the warning threshold. If it exceeds the warning threshold, give a warning.

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