Ship bearing fault diagnosis method based on parallel scaling Gramb angle field and CNN (Convolutional Neural Network)

Abstract

The invention discloses a ship bearing fault diagnosis method based on a parallel scaling Gramer angle field and a CNN, and the method comprises the steps: carrying out the parallel scaling fusion of a global vibration sequence and a segmented vibration sequence of the vibration fault data of each bearing, and obtaining a bearing one-dimensional time sequence signal; performing space polar coordinate transformation on the bearing one-dimensional time sequence signal to obtain an angle cosine value of a corresponding vibration sampling point in each section of bearing one-dimensional vibration time sequence signal; on the basis of the angle cosine values of the vibration sampling points, obtaining an RGB three-channel image sample set of the ship bearing fault according to a Grubrum matrix; and constructing a CNN bearing fault diagnosis model based on multi-convolution block stacking combined with star operation, obtaining an optimal CNN bearing fault diagnosis model according to the RGB three-channel image sample set, and realizing diagnosis and prediction of ship bearing faults according to the optimal CNN bearing fault diagnosis model. According to the invention, the problems of poor model learning effect, easy confusion between fault classes, low diagnosis accuracy and the like caused by insufficient bearing fault signal extraction precision and easy generation of an overfitting problem in a fault diagnosis task of an existing ship bearing fault diagnosis method are solved.

Description

Technical Field

[0001] The invention relates to the technical field of fault diagnosis of vulnerable parts of ship mechanical equipment, and in particular to a ship bearing fault diagnosis method based on parallel scaling Gram angle field and CNN. Background Art

[0002] Bearings are the most common and important transmission components in ship machinery and equipment. They are used in electric motors, induction generators, main shafts, gearboxes, hydraulic pumps, etc. Their operating status is directly related to the operating status of the ship's power propulsion system.

[0003] In the actual industrial production process, since bearings need to operate continuously under high speed and high load conditions, and ships are in harsh environments such as salt spray, vibration, and humidity for a long time, bearings are easily affected by corrosion, lubrication, foreign matter, etc., which will lead to failures. If not handled in time, it will cause huge safety hazards. Therefore, efficient and accurate fault identification of bearings is an important part of the health monitoring of ship mechanical systems, which is of great significance to ensure the safe operation of ships, quickly locate faults, and reduce maintenance costs.

[0004] At present, when obtaining bearing operation data, due to the complex working environment, changeable working conditions and numerous excitation sources inside ship machinery, there are often many interference components in the bearing monitoring data, which makes the fault characteristics weak and not obvious, and the quality of the collected fault signal is low. Many scholars usually perform signal noise reduction processing before inputting historical monitoring data into the intelligent diagnosis model, but most signal processing methods have the problems of strong manual dependence and poor adaptability. In addition, in order to overcome the uncertainty brought by the process of manually selecting fault features, many scholars select neural network models for adaptive extraction and learning of fault features, but because the bearing fault signal is a one-dimensional time series data, the model is prone to overfitting in the fault diagnosis task, resulting in poor learning effect, easy confusion between fault classes, and low diagnostic accuracy. Summary of the invention

[0005] The present invention provides a ship bearing fault diagnosis method based on parallel scaling Gram angle field and CNN to overcome the above technical problems.

[0006] In order to achieve the above object, the technical solution of the present invention is:

[0007] A ship bearing fault diagnosis method based on parallel scaling Gram angle field and CNN specifically includes the following steps:

[0008] S1: Obtain normal time-series vibration data of ship bearings under different loads;

[0009] Acquire bearing vibration fault data of different fault categories with abnormal time domain characteristics based on normal time series vibration data;

[0010] S2: Perform parallel scaling and fusion on the global vibration sequence and segmented vibration sequence of each bearing vibration fault data to obtain a new one-dimensional time series signal of the bearing;

[0011] Wherein, the global vibration sequence is the original bearing one-dimensional vibration time series signal in the bearing vibration fault data;

[0012] The segmented vibration sequence is a vibration sequence obtained by segmenting the original bearing one-dimensional vibration time series signal according to a preset segmentation method;

[0013] And the new bearing one-dimensional time series signal includes the global signal distribution and the local signal distribution of the original bearing one-dimensional vibration time series signal;

[0014] S3: Performing spatial polar coordinate transformation on the one-dimensional time series signal of the bearing to obtain the angular cosine value of the corresponding vibration sampling point in each segment of the one-dimensional vibration time series signal of the bearing;

[0015] S4: Based on the angular cosine value of the vibration sampling point, the one-dimensional vibration time series signal of each bearing segment is converted into the corresponding Gram matrix, and the RGB three-channel image sample set of the ship bearing fault is obtained according to the Gram matrix;

[0016] S5: Construct a CNN bearing fault diagnosis model based on multi-convolutional block stacking combined with star operations, and obtain the optimal CNN bearing fault diagnosis model based on the RGB three-channel image sample set;

[0017] S6: Based on the optimal CNN bearing fault diagnosis model, the diagnosis and prediction of ship bearing faults are realized.

[0018] Further, the S2 specifically includes the following steps:

[0019] S21: Perform global scaling on the global vibration signal of the bearing vibration fault data to obtain a global scaling sequence, which is expressed as follows:

[0020]

[0021] Where: represents the value of the i-th vibration sampling point after global scaling; f i represents the value of the i-th vibration sampling point of the original global sequence; F represents the set of values ​​of the original global sequence; N represents the number of vibration sampling points of the original global sequence; represents the global scaling sequence;

[0022] S22: Perform local scaling on the segmented vibration sequence of the bearing vibration fault data to obtain a local scaling sequence, which is expressed as follows:

[0023]

[0024] Where: Represents the value of the i-th vibration sampling point after local scaling; represents the value of the mth vibration sampling point of the jth local scaling sequence; m represents the length of the local scaling sequence of the bearing vibration fault data; e i represents the value of the i-th vibration sampling point of the original global sequence; E j represents the set of values ​​of the j-th local scaling sequence; represents a local scaling sequence;

[0025] S23: performing parallel scaling fusion on the global scaling sequence and the local scaling sequence to obtain a one-dimensional time series signal of the bearing;

[0026] The expression for parallel scaling fusion is:

[0027]

[0028]

[0029] Where: λ represents the design parameter; Represents the one-dimensional time series signal of the bearing after parallel scaling and fusion.

[0030] Further, the S4 specifically includes the following steps:

[0031] S41: based on the angular cosine value of the vibration sampling point, convert the one-dimensional vibration time series signal of each bearing segment into a corresponding Gram matrix;

[0032] The expression of the angular cosine value of the vibration sampling point is:

[0033]

[0034] The corresponding Gram matrix expression is

[0035]

[0036] Where: I represents the unit row vector and I = [1, 1, ..., 1]; θ i express The angular cosine value mapped after the numerical value is transformed into spatial polar coordinates; i represents the vibration sampling point of the bearing one-dimensional vibration time series signal and i = 1, 2, 3, ..., m; Represents parallel fusion scaling sequence The transpose of

[0037] S42: Obtain a sample set of RGB three-channel images of ship bearing faults according to the Gram matrix.

[0038] Furthermore, the construction method of the CNN bearing fault diagnosis model based on multi-convolutional block stacking combined with star operation described in S5 is specifically as follows:

[0039] Constructing a network layer structure for bearing fault diagnosis based on stacking multiple convolutional blocks;

[0040] And the network layer structure includes an input layer, an initial convolution block, a multi-level feature extraction convolution block and an output classification layer;

[0041] The input layer is used to input the sample data in the RGB three-channel image sample set into the initial convolution block;

[0042] The initial convolution block is used to extract the ship bearing fault features in the sample data;

[0043] The extracted ship bearing fault features are regularized and nonlinearly activated through the BN / ReLU6 layer;

[0044] The multi-level feature extraction convolution block includes a plurality of feature extraction convolution blocks connected in sequence;

[0045] Each of the feature extraction convolution blocks has a different convolution kernel to extract ship bearing fault features of different scales;

[0046] The feature extraction convolution block includes a convolution layer for further extracting the ship bearing fault feature and a blocks layer for high-bit mapping the output of the convolution layer;

[0047] The output classification layer is used to extract the output of the convolution block according to the multi-level features, predict and obtain the fault type of the ship bearing;

[0048] The feature space dimensions in the initial convolution block and the feature extraction convolution block are optimized through star operation to reconstruct the network layer structure to obtain a compact and lightweight network, which is used as the CNN bearing fault diagnosis model.

[0049] Further, in S5, the optimal CNN bearing fault diagnosis model is obtained according to the RGB three-channel image sample set, which specifically includes the following steps:

[0050] S51: randomly divide the RGB three-channel image sample set and obtain a data training set and a data test set;

[0051] S52: inputting the data training set into the CNN bearing fault diagnosis model for model training, and obtaining the trained CNN bearing fault diagnosis model;

[0052] S53: Based on the cross entropy loss function as the model loss function, the trained CNN bearing fault diagnosis model is tested through the data test set, and it is determined whether the output of the trained CNN bearing fault diagnosis model converges;

[0053] If it is confirmed that the output of the trained CNN bearing fault diagnosis model converges, then the trained CNN bearing fault diagnosis model is the optimal CNN bearing fault diagnosis model;

[0054] Otherwise, based on the Adam optimization algorithm, the network weight parameters of the trained CNN bearing fault diagnosis model are updated, and steps S51 to S52 are repeated.

[0055] Beneficial effect: The present invention provides a ship bearing fault diagnosis method based on parallel scaling Gram angular field and CNN. The global vibration sequence and segmented vibration sequence of the bearing vibration fault data are parallel scaled and fused to obtain the one-dimensional time series signal of the bearing, and the vibration sampling points are transformed into spatial polar coordinates to obtain the Gram matrix, thereby obtaining the RGB three-channel image sample set of the ship bearing fault. The present invention filters the Gaussian noise of the original vibration signal through the Gram image transformation, and the spatial polar coordinate transformation can effectively distinguish the fault characteristics and noise of the ship bearing. The Gram matrix is ​​composed of multiple groups of two-vector inner products, which can maintain the time dependence of the time series. When facing the ship machinery fault data as a non-stationary time series signal, the Gram angle field can alleviate the influence of noise and completely preserve the The information time correlation is retained, the noise interference is reduced and irrelevant information is removed for the vibration signal of the ship bearing fault; in addition, the present invention generates a two-dimensional image sample set with time information, namely, an RGB three-channel image sample set, through the Gram coding technology of parallel scaling and fusion operations, so that the generated fault image sample set has a local field of view and a global field of view, and the relative relationship between each vibration sampling point in the sample is obtained while maintaining the continuous correlation between samples in the time series, which greatly improves the sample quality and better expresses the fault feature information in the vibration signal. In addition, by constructing a compact CNN bearing fault diagnosis model based on multi-convolution block stacking combined with star operation, while ensuring the prediction efficiency of bearing fault diagnosis, the visual recognition advantage of the CNN model can be better utilized to accurately identify ship bearing faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0057] Figure 1 The present invention is a flow chart of a ship bearing fault diagnosis method based on parallel scaling Gram angle field and CNN;

[0058] Figure 2It is a schematic diagram of the ship bearing vibration signal in this embodiment after parallel scaling and fusion Gram image coding technology;

[0059] Figure 3 This is a network structure diagram of the CNN model for ship bearing fault diagnosis in this embodiment;

[0060] Figure 4 This is a fault data pattern diagram of the first type of abnormal time domain characteristics in this embodiment;

[0061] Figure 5 This is a fault data pattern diagram of the second type of abnormal time domain characteristics in this embodiment;

[0062] Figure 6 This is a fault data pattern diagram of the third type of abnormal time domain characteristics in this embodiment;

[0063] Figure 7 This is a fault data pattern diagram of the fourth type of abnormal time domain characteristics in this embodiment;

[0064] Figure 8 This is a comparison chart of the fault diagnosis effects of applying parallel scaling and fusion of Gram images and other image conversion methods in this embodiment. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] This embodiment provides a ship bearing fault diagnosis method based on parallel scaling Gram angle field and CNN, such as Figure 1 As shown, the specific steps include:

[0067] S1: Set the sampling time and the number of vibration sampling points of the ship bearing in the Cartesian coordinate system to collect and obtain the normal time series vibration data of the ship bearing under different loads through the vibration sensor; obtain the bearing vibration fault data of different fault categories with abnormal time domain characteristics according to the normal time series vibration data;

[0068] The specific steps include:

[0069] S11: Obtain vibration data from the actual ship Wärtsilä W8L20 engine bearing as the basis for model training and testing data. The engine speed is 1000rpm and the maximum load is 1542kW. The vibration sensor installed on the engine bearing cover transmits the vibration data to the ship monitoring system, including normal operating data under five loads: 25%, 50%, 75%, 85%, and 100%.

[0070] S12: Generate synthetic data by inserting abnormal amplitudes based on the vibration signal of normal operating data to form abnormal amplitude fault symptoms, such as Figures 4 to 7 As shown, according to the two types of abnormal manifestations, point abnormalities and collective abnormalities, a total of four data patterns of time domain characteristic abnormalities are simulated, of which the first three fault data are formed by point abnormalities, and the fourth fault is formed by collective abnormalities. Based on the analysis of healthy time series data, the abnormal range of point abnormal vibration amplitude is determined to be [2,6], the position of point abnormality in the sequence is randomly determined, the mean of collective abnormality is set to 0, and the variance range is determined to be [1,6], and finally different fault time series are formed;

[0071] S2: Perform parallel scaling and fusion on the global vibration sequence and segmented vibration sequence of each bearing vibration fault data to obtain a new bearing one-dimensional time series signal; wherein, the global vibration sequence is the original bearing one-dimensional vibration time series signal in the bearing vibration fault data; the segmented vibration sequence is the vibration sequence obtained by segmenting the original bearing one-dimensional vibration time series signal according to a preset segmentation method; and the preset segmentation method is to segment the global vibration sequence according to the preset number of vibration signal points in each segmented vibration sequence to obtain the segmented vibration sequence, for example, the length of the global vibration sequence is L, if each segmented vibration sequence is preset to include m signal points, it is divided into j segments, that is, there are m signal points in each segment; the new bearing one-dimensional time series signal includes the global signal distribution and local signal distribution of the original bearing one-dimensional vibration time series signal;

[0072] like Figure 2 As shown, the specific steps include the following steps

[0073] S21: Perform global scaling on the global vibration signal of the bearing vibration fault data to obtain a global scaling sequence, which is expressed as follows:

[0074]

[0075] Where: represents the value of the i-th vibration sampling point after global scaling; f i represents the value of the i-th vibration sampling point of the original global sequence; F represents the set of values ​​of the original global sequence; N represents the number of vibration sampling points of the original global sequence; It represents the global scaling sequence, which includes the changing trend of the overall time series signal and the correlation between each time point. It can retain the influence of each point in the global fault information in the Gram matrix without involving matrix operations and increasing the amount of matrix calculation.

[0076] S22: Perform local scaling on the segmented vibration sequence of the bearing vibration fault data to obtain a local scaling sequence, which is expressed as follows:

[0077]

[0078] Where: Represents the value of the i-th vibration sampling point after local scaling; represents the value of the mth vibration sampling point of the jth local scaling sequence; m represents the length of the local scaling sequence of the bearing vibration fault data; e i represents the value of the i-th vibration sampling point of the original global sequence; E j represents the set of values ​​of the j-th local scaling sequence; Represents a local scaling sequence, and completes fine-grained correlation extraction for the change process of the local time series signal to focus on the detailed trend of each time point in the short time series. It can essentially be regarded as a slicing operation of the vibration signal, and the final Gram matrix size is determined by the size of m in this formula;

[0079] S23: performing parallel scaling fusion on the global scaling sequence and the local scaling sequence to obtain a one-dimensional time series signal of the bearing;

[0080] The expression for parallel scaling fusion is:

[0081]

[0082] Where: represents the one-dimensional time series signal of the bearing after parallel scaling fusion; λ represents the design parameter, and its value range is selected from the fused There may be a phenomenon beyond the [0,1] interval, resulting in the subsequent θ i The result is not within the monotonic interval of the cosine function, which affects the bijectivity of the Gram angle field and cannot guarantee the absolute time relationship of the fault data points. Normalization is performed and used as the final processing object of polar coordinate transformation. In this embodiment, the value of λ is 0.5;

[0083] S3: Performing spatial polar coordinate transformation on the one-dimensional time series signal of the bearing to obtain the angular cosine value of the corresponding vibration sampling point in each segment of the one-dimensional vibration time series signal of the bearing;

[0084] Specifically, the time series The timestamp and the value of the vibration sampling point are mapped from the Cartesian coordinate system to the polar coordinate system to obtain the radius and angle. The purpose is to distinguish the fault characteristic value information of the ship bearing and Gaussian noise through spatial transformation. The expression is:

[0085]

[0086] Where: θ i express The angular cosine of the value after spatial transformation, and its value range is [0,π]; r i express The radius of the timestamp transformation mapping corresponding to the value;

[0087] S4: Based on the angular cosine value of the vibration sampling point, the one-dimensional vibration time series signal of each bearing segment is converted into the corresponding Gram matrix, and the RGB three-channel image sample set of the ship bearing fault is obtained according to the Gram matrix;

[0088] The specific steps include:

[0089] S41: In this embodiment, each converted one-dimensional bearing vibration time series signal segment is converted into a corresponding Gram matrix, and then the standardized Gram matrix is ​​converted into an image; that is, based on the angular cosine value of the vibration sampling point, the angle between any two vibration sampling points is cosine transformed to obtain the correlation between each time point, so as to obtain each segment of the bearing one-dimensional vibration time series signal converted into the corresponding Gram matrix, which is expressed as follows:

[0090]

[0091] Where: I represents the unit row vector and I = [1, 1, ..., 1]; θ i express The angular cosine value mapped after the numerical value is transformed into spatial polar coordinates; i represents the vibration sampling point of the bearing one-dimensional vibration time series signal and i=1,2,3,...,m; Represents parallel fusion scaling sequence The transpose of

[0092] S42: Obtain a sample set of RGB three-channel images of ship bearing faults according to the Gram matrix;

[0093] Specifically, the Gram matrix is ​​regarded as a two-dimensional array and converted into an RGB three-channel color image in Matlab based on the Gram coding technology. In the parallel scaling Gram angular field image, time increases as the coding position moves from the upper left corner of the image to the lower right corner. Therefore, the time dimension is included in the geometric structure of the image through the parallel scaling Gram angular field coding. This is the source of the time dependence of the parallel scaling Gram angular field, thereby reflecting the dynamic change of information. The implementation method of converting to an RGB three-channel color image in Matlab based on the Gram coding technology is an existing well-known technical means, which will not be described in detail here.

[0094] S5: Construct a CNN bearing fault diagnosis model based on multi-convolutional block stacking combined with star operations, which can map the input to a high-dimensional, nonlinear feature space without expanding the network, and obtain the optimal CNN bearing fault diagnosis model based on the RGB three-channel image sample set;

[0095] In a specific embodiment, Figure 3 As shown in the figure, the construction method of the CNN bearing fault diagnosis model based on multi-convolutional block stacking combined with star operation is as follows:

[0096] Constructing a network layer structure for bearing fault diagnosis based on stacking multiple convolutional blocks;

[0097] And the network layer structure includes an input layer, an initial convolution block, a multi-level feature extraction convolution block and an output classification layer;

[0098] The input layer is used to input the sample data in the RGB three-channel image sample set into the initial convolution block;

[0099] The initial convolution block is used to extract the ship bearing fault features in the sample data;

[0100] The extracted ship bearing fault features are regularized and nonlinearly activated through the BN / ReLU6 layer;

[0101] The multi-level feature extraction convolution block includes a plurality of feature extraction convolution blocks connected in sequence;

[0102] Each of the feature extraction convolution blocks has a different convolution kernel to extract ship bearing fault features of different scales;

[0103] The feature extraction convolution block includes a convolution layer for further extracting the ship bearing fault feature and a blocks layer for high-bit mapping the output of the convolution layer;

[0104] The output classification layer is used to extract the output of the convolution block according to the multi-level features, predict and obtain the fault type of the ship bearing;

[0105] The feature space dimensions in the initial convolution block and the feature extraction convolution block are optimized through star operations to reconstruct the network layer structure to obtain a compact and lightweight network, which is used as a CNN bearing fault diagnosis model.

[0106] The star operation is used to optimize the feature space dimensions in the initial convolution block and the feature extraction convolution block, specifically:

[0107] S01: Take any feature extraction convolution block in the initial convolution block or the multi-level feature extraction convolution block as a single-layer neural network, merge the weight matrix W1, W2 and the bias terms B1, B2 of the single-layer neural network, set X as its input feature and let x represent the element in the input feature X, then the star operation can be expressed as:

[0108]

[0109] S02: For a single-layer neural network with a single output channel and a single feature element input x, the number of output channels of the single-layer neural network is expanded to rewrite the star operation, and the feature space dimension is optimized based on the expanded and rewritten star operation. The expression is:

[0110]

[0111] Where: x h ,x s Respectively represent the weight matrix of the hth channel, the weight matrix of the sth channel, the input of the hth channel, and the input of the sth channel; w1,w2,x∈R (d+1)×1 represents the weight of a single output channel; d represents the number of input channels; where the weight matrix W1,W2∈R after expanding the output channel (d+1)×(d′+1) , input feature X∈R (d+1)×n ; h, s represent channel index; represents the coefficient of the term and is The simplified form of is:

[0112]

[0113] In this embodiment, after star operation in a single-layer neural network, we finally have different items, except In addition to the term, each term is an independent, implicit dimension that maintains a nonlinear association with the sample data x in the input feature X. Similarly, when the model is stacked with t layers, the maximum feature space dimension will reach Therefore, when the model stacks multiple layers with star operations (even if only a few layers), the star operation will significantly amplify the implicit feature space dimension exponentially without incurring additional computational costs, thus easily building a compact, lightweight, and efficient network structure based on the star operation;

[0114] In a specific embodiment, S5 obtains the optimal CNN bearing fault diagnosis model according to the RGB three-channel image sample set, which specifically includes the following steps:

[0115] S51: randomly divide the RGB three-channel image sample set and obtain a data training set and a data test set;

[0116] Specifically, every 512 data points in the RGB three-channel image sample set are truncated to generate a 224×224 RGB three-channel fault sample image. The bearing data set under five working conditions contains a total of 3000 sample images, which are divided into training sets and test sets in a ratio of 4:1. Each fault type consists of 96 training samples and 24 test samples, as shown in Table 1.

[0117] Table 1. Division table of data RGB three-channel image sample set

[0118]

[0119] S52: inputting the data training set into the CNN bearing fault diagnosis model for model training, and obtaining the trained CNN bearing fault diagnosis model;

[0120] Specifically, the Gram image dataset is divided into a data training set and a data test set. The data training set is input into the intelligent diagnosis network model for optimization training to generate a trained CNN ship bearing fault diagnosis model, and the data test set is used to verify the fault classification effect of the trained network model.

[0121] S53: Based on the cross entropy loss function as the model loss function, the trained CNN bearing fault diagnosis model is tested through the data test set, and it is determined whether the output of the trained CNN bearing fault diagnosis model converges;

[0122] If it is confirmed that the output of the trained CNN bearing fault diagnosis model converges, then the trained CNN bearing fault diagnosis model is the optimal CNN bearing fault diagnosis model;

[0123] Otherwise, based on the Adam optimization algorithm, the network weight parameters of the trained CNN bearing fault diagnosis model are updated, and steps S51 to S52 are repeated.

[0124] In this embodiment, the Adam optimization algorithm is used to update the network training parameters, the initial value of the learning rate is 0.001, and ReduceLROnPlateau is used to update the learning rate to achieve the self-attenuation process of the learning rate. The accuracy of the data test set is used as the adjustment index, and the patience in ReduceLROnPlateau is selected as 4. The cross entropy loss function is used to calculate the model loss. The neuron discard rate in the model is set to 0.2, the training batch size is 64, the classifier is softmax, and the activation function is ReLU6;

[0125] S6: Based on the optimal CNN bearing fault diagnosis model, the diagnosis and prediction of ship bearing faults are realized.

[0126] In order to verify the superior information representation capability of the parallel scaled Gram angular field, this embodiment compares the diagnostic effects of different image samples as data sets after being input into the model. In addition to the original Gram technology, continuous wavelet transform (wavelet basis is complex Morlet wave) and short-time Fourier transform (window function is Hanning window) which are widely used in the field of signal representation are selected as controls. On the basis of ensuring that the data set, model, hyperparameters, and loss function settings are consistent, a comparison chart of the fault diagnosis accuracy of each sample after being input into the model under various working conditions is obtained, as shown in FIG. Figure 8 shown.

[0127] In this embodiment, the Gaussian noise of the original vibration signal is filtered through the Gram image transformation, and the spatial polar coordinate transformation can effectively distinguish the fault characteristics and noise of the ship bearing. The Gram matrix is ​​composed of multiple groups of two-vector inner products, which can maintain the time dependence of the time series. When facing the ship machinery fault data as a non-stationary time series signal, the Gram angle field can alleviate the noise impact, completely retain the information time correlation, reduce the noise interference for the ship bearing fault vibration signal and remove irrelevant information; in addition, the present invention generates a two-dimensional image sample set with time information, that is, an RGB three-channel image sample set, through the Gram coding technology of parallel scaling and fusion operations, so that the generated fault image sample set has a local field of view and a global field of view, and obtains the relative relationship between each vibration sampling point in the sample while maintaining the continuous correlation between the samples in the time series, which greatly improves the sample quality and better expresses the fault feature information in the vibration signal. In addition, by constructing a compact CNN bearing fault diagnosis model based on multi-convolution block stacking combined with star operation, while ensuring the prediction efficiency of bearing fault diagnosis, the visual recognition advantage of the CNN model can be better utilized to accurately identify ship bearing faults.

[0128] Finally, 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A ship bearing fault diagnosis method based on parallel scaling Gram angle field and CNN, characterized in that: The specific steps include: S1: Obtain normal time-series vibration data of ship bearings under different loads; Acquire bearing vibration fault data of different fault categories with abnormal time domain characteristics based on normal time series vibration data; S2: Perform parallel scaling and fusion on the global vibration sequence and segmented vibration sequence of each bearing vibration fault data to obtain a new one-dimensional time series signal of the bearing; The global vibration sequence is the original bearing one-dimensional vibration time series signal in the bearing vibration fault data; the segmented vibration sequence is the vibration sequence obtained by segmenting the original bearing one-dimensional vibration time series signal according to a preset segmentation method; And the new bearing one-dimensional time series signal includes the global signal distribution and the local signal distribution of the original bearing one-dimensional vibration time series signal; S3: Performing spatial polar coordinate transformation on the one-dimensional time series signal of the bearing to obtain the angular cosine value of the corresponding vibration sampling point in each segment of the one-dimensional vibration time series signal of the bearing; S4: Based on the angular cosine value of the vibration sampling point, the one-dimensional vibration time series signal of each bearing segment is converted into the corresponding Gram matrix, and the RGB three-channel image sample set of the ship bearing fault is obtained according to the Gram matrix; S5: Construct a CNN bearing fault diagnosis model based on multi-convolutional block stacking combined with star operations, and obtain the optimal CNN bearing fault diagnosis model based on the RGB three-channel image sample set; S6: Based on the optimal CNN bearing fault diagnosis model, the diagnosis and prediction of ship bearing faults are realized.

2. A ship bearing fault diagnosis method based on parallel scaling Gram angle field and CNN according to claim 1, characterized in that: The S2 specifically includes the following steps S21: Perform global scaling on the global vibration signal of the bearing vibration fault data to obtain a global scaling sequence, which is expressed as follows: Where: represents the value of the i-th vibration sampling point after global scaling; f i represents the value of the i-th vibration sampling point of the original global sequence; F represents the set of values ​​of the original global sequence; N represents the number of vibration sampling points of the original global sequence; represents the global scaling sequence; S22: Perform local scaling on the segmented vibration sequence of the bearing vibration fault data to obtain a local scaling sequence, which is expressed as follows: Where: Represents the value of the i-th vibration sampling point after local scaling; represents the value of the mth vibration sampling point of the jth local scaling sequence; m represents the length of the local scaling sequence of the bearing vibration fault data; e i represents the value of the i-th vibration sampling point of the original global sequence; E j represents the set of values ​​of the j-th local scaling sequence; represents a local scaling sequence; S23: performing parallel scaling fusion on the global scaling sequence and the local scaling sequence to obtain a one-dimensional time series signal of the bearing; The expression for performing parallel scaling fusion is: Where: λ represents the design parameter; Represents the one-dimensional time series signal of the bearing after parallel scaling and fusion.

3. A ship bearing fault diagnosis method based on parallel scaling Gram angle field and CNN according to claim 2, characterized in that: The S4 specifically includes the following steps S41: based on the angular cosine value of the vibration sampling point, convert the one-dimensional vibration time series signal of each bearing segment into a corresponding Gram matrix; The expression of the angular cosine value of the vibration sampling point is: The corresponding Gram matrix expression is Where: I represents the unit row vector and I = [1, 1, ..., 1]; θ i express The angular cosine value mapped after the numerical value is transformed into spatial polar coordinates; i represents the vibration sampling point of the bearing one-dimensional vibration time series signal and i=1,2,3,...,m; Represents a parallel fusion scaling sequence The transpose of S42: Obtain a sample set of RGB three-channel images of ship bearing faults according to the Gram matrix.

4. A ship bearing fault diagnosis method based on parallel scaling Gram angle field and CNN according to claim 3, characterized in that: The construction method of the CNN bearing fault diagnosis model based on multi-convolutional block stacking combined with star operation described in S5 is specifically as follows: Constructing a network layer structure for bearing fault diagnosis based on stacking multiple convolutional blocks; And the network layer structure includes an input layer, an initial convolution block, a multi-level feature extraction convolution block and an output classification layer; The input layer is used to input the sample data in the RGB three-channel image sample set into the initial convolution block; The initial convolution block is used to extract the ship bearing fault features in the sample data; The extracted ship bearing fault features are regularized and nonlinearly activated through the BN / ReLU6 layer; The multi-level feature extraction convolution block includes a plurality of feature extraction convolution blocks connected in sequence; Each of the feature extraction convolution blocks has a different convolution kernel to extract ship bearing fault features of different scales; The feature extraction convolution block includes a convolution layer for further extracting the ship bearing fault feature and a blocks layer for high-bit mapping the output of the convolution layer; The output classification layer is used to extract the output of the convolution block according to the multi-level features, predict and obtain the fault type of the ship bearing; The feature space dimensions in the initial convolution block and the feature extraction convolution block are optimized through star operation to reconstruct the network layer structure to obtain a compact and lightweight network, which is used as the CNN bearing fault diagnosis model.

5. A ship bearing fault diagnosis method based on parallel scaling Gram angle field and CNN according to claim 3, characterized in that: In S5, the optimal CNN bearing fault diagnosis model is obtained based on the RGB three-channel image sample set, which specifically includes the following steps: S51: randomly divide the RGB three-channel image sample set and obtain a data training set and a data test set; S52: inputting the data training set into the CNN bearing fault diagnosis model for model training, and obtaining the trained CNN bearing fault diagnosis model; S53: Based on the cross entropy loss function as the model loss function, the trained CNN bearing fault diagnosis model is tested through the data test set, and it is determined whether the output of the trained CNN bearing fault diagnosis model converges; If it is confirmed that the output of the trained CNN bearing fault diagnosis model converges, then the trained CNN bearing fault diagnosis model is the optimal CNN bearing fault diagnosis model; Otherwise, based on the Adam optimization algorithm, the network weight parameters of the trained CNN bearing fault diagnosis model are updated, and steps S51 to S52 are repeated.

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