CN113378943A - Engine rotor rubbing fault diagnosis method based on wavelet-gray level co-occurrence matrix - Google Patents
Engine rotor rubbing fault diagnosis method based on wavelet-gray level co-occurrence matrix Download PDFInfo
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Abstract
The invention provides an engine rotor rubbing fault diagnosis method based on a wavelet-gray level co-occurrence matrix, and solves the problems of low fault identification precision, high requirements of a neural network method on samples and hardware and complex training of the traditional method. The method comprises the following specific steps: 1) collecting engine friction data; 2) performing wavelet transformation on the engine rub-impact data to obtain a vibration signal wavelet transformation diagram; 3) converting the wavelet transform map of the vibration signal into a gray scale map, extracting the gray scale co-occurrence matrix image characteristic quantity of the gray scale map, and constructing a characteristic vector; 4) setting labels for the feature vectors, and dividing the feature vectors into a training set and a test set; 5) inputting the training set into a machine learning algorithm support vector machine for training to obtain a trained support vector machine; 6) and inputting the test set into a trained support vector machine to realize the collision and abrasion fault diagnosis of the engine rotor.
Description
Technical Field
The invention belongs to the technical field of engine fault diagnosis, and particularly relates to an engine rotor rubbing fault diagnosis method based on a wavelet-gray level co-occurrence matrix.
Background
The high-speed rotor in modern aeroengines is mostly designed by a flexible shaft, the working speed is generally more than 10000r/min, the working speed of some small engines is as high as 40000-50000 r/min, and for such high-speed engines, the vibration and stability problems of the rotor-supporting system are the key points of structural integrity and reliability under the working condition that the rotor system is in bending-torsion coupling. The dynamic and static collision and friction of a rotor system is a serious fault which often occurs in a rotary machine; the vibration is aggravated when the rubbing occurs, the gradual evolution of local rubbing can cause the rubbing of the whole circumference, even the damage of the whole shafting can be caused, the system can not normally operate, and the direct economic loss and the indirect economic loss are very huge. Therefore, it is very important to evaluate the operation state of the rotary machine during rubbing and to diagnose the rubbing faults in the research on the dynamic and static rubbing faults of the rotor system.
The conventional method comprises time domain analysis, wavelet transformation, EMD decomposition and the like, the conventional method extracts vibration fault features through a signal processing method, so that fault information is lost, and fault identification precision is lowered.
With the rapid development of image processing and deep learning technologies, learners use a two-dimensional graph as input of a neural network for fault diagnosis, and those who construct tigers and the like obtain a time-frequency graph by performing continuous wavelet transformation on a vibration signal and input the compressed time-frequency graph into a Convolutional Neural Network (CNN), so that intelligent diagnosis of a rolling bearing is realized.
Therefore, it is necessary to design a new method for diagnosing the rubbing fault of the engine rotor.
Disclosure of Invention
The invention aims to solve the problems of low fault identification precision, high requirements of a neural network method on samples and hardware and complex training of the traditional method, and provides an engine rotor rub-impact fault diagnosis method based on a wavelet-gray level co-occurrence matrix.
In order to achieve the purpose, the technical solution provided by the invention is as follows:
the engine rotor rub-impact fault diagnosis method based on the wavelet-gray level co-occurrence matrix is characterized by comprising the following steps of:
1) collecting engine friction data;
2) performing wavelet transformation on the engine rub-impact data obtained in the step 1) to obtain a vibration signal wavelet transformation diagram;
3) converting the wavelet transformation map of the vibration signals obtained in the step 2) into a gray scale map, extracting the gray scale co-occurrence matrix image characteristic quantity of the gray scale map, and constructing a characteristic vector;
4) setting labels for the feature vectors obtained in the step 3), and dividing the feature vectors into a training set and a test set;
5) inputting the training set obtained in the step 4) into a machine learning algorithm support vector machine for training, and inputting the test set into the trained support vector machine for verification to obtain a verified support vector machine;
6) inputting the detection sample into the support vector machine verified in the step 5), diagnosing the collision and abrasion fault of the engine rotor, and outputting the result of the collision and abrasion fault diagnosis of the engine rotor.
Further, the step 2) is specifically as follows:
defining psi (t) e L2(R) satisfies the condition
In the formula, psi (omega) is Fourier transform of psi (t), psi (t) is a wavelet mother function or basic wavelet function, and the wavelet function is obtained by expansion and translation
In the formula: α is a scale factor (or scale factor) and τ is a translation factor;
ψα,τ(t) is a wavelet basis function depending on alpha and tau, and a group of function sequences are obtained by performing telescopic translation on a group of mother functions psi (t);
defining a presence signal x (t) e L2(R) inner-product it with the wavelet basis function, i.e.:
the continuous wavelet transform of formula (3) is x (t) and is denoted as CTW. Psiα,τThe effect of (t) can be seen as a lens, where τ is equivalent to translating the lens relative to the target and α is equivalent to advancing or retracting the lens toward or away from the target.
Further, in step 3), the image feature quantities include contrast, energy, entropy and correlation; calculating the mean value and standard deviation of the image characteristic quantities, and extracting characteristic vectors;
the contrast is the definition of the reflected image and is reflected by the depth of the image texture grooves, and the contrast is as follows:
wherein p (i, j) refers to the normalized gray level co-occurrence matrix;
the energy is calculated by the sum of squares of the element values in the gray level co-occurrence matrix, and is also called energy, which is the gray level distribution of the representation image. If all the element values of the gray level co-occurrence matrix are the same value, the smaller the value obtained by energy calculation is; on the contrary, if the distribution of the element values in the gray level co-occurrence matrix is not different greatly, the value obtained by energy calculation is larger; energy is represented by ASM, which represents the regularly changing texture pattern of an image. The formula is as follows:
the entropy is a measure representing information contained in an image, and the texture information is also image information, so that the texture information is a measure belonging to randomness, when all element values in the gray level co-occurrence matrix are values and all elements have great randomness, the entropy is large, and the distribution of the elements in the matrix is relatively dispersed, which reflects the complexity of the image texture, and the formula is as follows:
the correlation is the similarity of elements of the gray level co-occurrence matrix in different directions, the value of the correlation reflects the correlation of local pixel values, when the value distribution difference of the gray level co-occurrence matrix is large, the correlation value is larger, and the formula is as follows:
wherein
Further, in step 4), the feature vectors are divided into a training set and a test set according to a 7: 3 sample division ratio.
The invention has the advantages that:
the fault diagnosis method combines the traditional signal processing method with the computer vision image processing, extracts the image fault of the time-frequency graph obtained by the traditional signal processing method by the image processing method, inputs the extracted fault characteristics into the support vector machine algorithm for fault recognition and diagnosis, solves the problem of fault characteristic loss caused by the traditional fault characteristic extraction method, avoids the problems of long time consumption, complex and fussy training process and the like of using a neural network, and effectively improves the precision of fault diagnosis. In addition, the image processing method is applied to the aspect of fault diagnosis of the aircraft engine, and a new thought is provided for engine state detection and fault diagnosis.
Drawings
FIG. 1 is a diagram of a wavelet transform portion of a vibration signal;
FIG. 2 is a diagram of classification results;
FIG. 3 is an overall flow chart of the method of the present invention;
FIG. 4 is a grayscale image;
FIG. 5 is a gray level co-occurrence matrix;
FIG. 6 is an optimal generalized classification surface for a linear undifferentiated case;
fig. 7 is a nonlinear to linear conversion.
Detailed Description
The invention is described in further detail below with reference to the following figures and specific examples:
step 1:
performing wavelet transformation on engine friction data (namely vibration data) acquired by an experiment to obtain a vibration signal wavelet transformation graph (relevant information contained in vibration is displayed in the wavelet graph);
defining psi (t) e L2(R) satisfies the condition
Where Ψ (ω) is a Fourier transform of Ψ (t), which is a wavelet mother function or a basic wavelet function. The wavelet function is subjected to expansion and translation to obtain
In the formula: α is a scale factor and τ is a scale factor. Psiα,τAnd (t) is a wavelet basis function depending on alpha and tau, and is a group of function sequences obtained by performing telescopic translation on a group of mother functions psi (t).
Let there be a signal x (t) e L2(R) inner product it with wavelet basis function, i.e.:
the continuous wavelet transform of formula (3) is x (t) and is denoted as CTW. Psiα,τThe effect of (t) can be seen as a lens, where τ is equivalent to translating the lens relative to the target and α is equivalent to advancing or retracting the lens toward or away from the target.
Step 2:
performing image processing on the wavelet transform map of the vibration signal, converting the wavelet transform map into a gray-scale map, extracting the gray-scale co-occurrence matrix image characteristic quantity of the gray-scale map, and constructing a characteristic vector;
the gray level co-occurrence matrix is a method for researching the spatial relationship of pixel values of an image, reflects the relevance of the pixels through the distance and the angle between the pixel values, is a matrix function containing the distance and the angle, integrates the information of the image in the direction, the interval, the change amplitude, the speed and the like, and is expressed through the matrix. As shown in fig. 4:
as shown in fig. 4 (a) which is a gray image, two points (x, y) and (x + a, y + b) are arbitrarily selected, the value of the point pair is set to (i, j), and the point (x, y) is scanned over the entire image, so that various (i, j) values are formed; for fig. 4 (a), the matrix in which the number of occurrences of each of (i, j) is recorded and then arranged into a matrix is a gray level co-occurrence matrix, as shown in fig. 4 (b), which is a gray level co-occurrence matrix representing (a); when a and b are different, different gray level co-occurrence matrices are formed, and (b) is a gray level co-occurrence matrix in which a is 1 and b is 0, which is a gray level co-occurrence matrix with a direction of 0 °, and the gray level co-occurrence matrices with four directions of 0 °, 45 °, 90 ° and 135 ° are calculated.
Assuming that the gray scale number is k, the combination of (i, j) has k ^2 types, generally for calculation convenience, the gray scale number is compressed, but for different gray scale numbers, the identification effect has certain difference, the compression of the gray scale number is small, so that the correlation between the gray scales is accidental, and different features cannot be extracted; larger size easily causes the feature of the similar image to be disordered.
The feature vectors are formed by calculating the mean and standard deviation of the feature quantities, which are usually characterized by scalar quantities, such as contrast, energy, entropy and correlation.
The resulting feature vectors are shown in the table:
TABLE 1 partial eigenvalues of gray level co-occurrence matrix
| ASM energy | Contrast ratio | Entropy of the entropy | Correlation |
| 2.26E-01 | 3.34E+00 | 2.04E+00 | 8.40E-01 |
| 1.89E-01 | 3.43E+00 | 2.40E+00 | 7.91E-01 |
| 2.25E-01 | 3.64E+00 | 2.05E+00 | 8.34E-01 |
| 1.86E-01 | 3.84E+00 | 2.41E+00 | 7.92E-01 |
And step 3:
setting labels for the eigenvectors obtained in the step 2), and dividing the eigenvectors into a training set and a test set according to a sample division ratio of 7: 3;
and 4, step 4:
inputting the training set obtained in the step 3) into a machine learning algorithm Support Vector Machine (SVM) for training, and inputting the test set into the trained SVM for verification to obtain a verified SVM;
the support vector machine is a learning method for realizing a decision function set by utilizing a structural risk minimization principle. It was developed from the optimal classification hyperplane in the linear separable case. By "hyperplane" is meant a collection of linear functions. When the hyperplane classification is adopted, there are two cases, one is that the classification surface between samples of different classes is truly linear, i.e. the samples are linearly separable; another is that the classification planes between samples are non-linear. In this case, the support vector machine can perform feature classification on various data by introducing a non-linear mapping function to map the samples into a high-dimensional linear space (called feature space), and then constructing a classification hyperplane in the space.
The basic idea of a support vector machine can be illustrated with figure 5. In the figure, circles and squares represent two types of samples;
h is a classification hyperplane, H1 and H2 are planes which pass through a sample closest to the classification hyperplane in each class and are parallel to the classification hyperplane, and the positions between the planes are called classification intervals. The optimal classification surface is required to not only correctly separate the two classes (training error rate is 0) but also maximize the classification interval. The classification surface equation is x · w + b ═ 0, which is normalized to make a linearly separable sample set (x ═ w ═ b)i,yi),i＝1,......,l,x∈RdY belongs to { +1, -1}, and satisfies
yi[(w·xi)+b]-1≥0,i＝1,2,...,l (12)
The classification interval is then equal to 2l ω min. Making the interval maximally equivalent to | | ω | | non-woven phosphor2To minimize it. Satisfies the conditions in the formula (12) and
the smallest classification surface is called the optimal classification surface, and the training sample points on H1 and H2 are called support vectors.
Vapnik gives the solution to the problem of finding the optimal classification surface, i.e. the classification function is
In the formula, the multiplier alphaiIs an objective function
Within constraint alphai≥0,i＝1,2,...,l
Maximum point of lower, non-zero alphaiCorresponding sample point xiFor support vectors, threshold values
b＝yi-ω·xi (16)
The above discussion is that linear problems, for which non-linear problems can be transformed into linear problems in a high-dimensional space by non-linearity (see fig. 6), are generally complex, but it is noted from the above discussion that neither the objective function (12) nor the classification function (13) actually need only be subjected to an inner product operation in the high-dimensional space. In fact, it is not necessary to know the exact form of this high dimensional space, as long as the transformed inner product operation is defined, without actually performing the transformation.
Statistical learning theory states that according to the Hilbert-Schznidt principle, as long as one operation satisfies the Mercer condition, it can be used as the inner product here. Merer conditions: for an arbitrary function k (u, v), it is a sufficient prerequisite for inner product operations in some feature space to be: for any one
And is
Is provided with
If k (u, v) is used to replace the dot product in the optimal classification hyperplane, the method is equivalent to transforming the original feature space to a new feature space, and the objective function at the moment becomes:
the corresponding classification function also becomes
The use of different kernel functions k (u, v) will result in different algorithms for support vector machines, and commonly used kernel functions are linear kernel functions, polynomial kernel functions, radial basis kernel functions, Sigmoid kernel functions.
The support vector machine transforms the input space into a high-dimensional space through the nonlinear transformation defined by the inner product function, and the optimal classification hyperplane is solved in the space. As shown in fig. 7, xiFor a support vector, x is an unknown vector, the support vector machine formally resembles a neural network in the classification function, the output of which is the inner product of a number of intermediate layer nodes corresponding to the input samples and a support vector, and is therefore also called a support vector network.
And 5:
inputting the detection sample into the support vector machine verified in the step 5), diagnosing the collision and abrasion fault of the engine rotor, and outputting whether the fault exists or not, wherein the figure 2 is a classification chart for identifying the fault by using the support vector machine, and the precision of the classification chart can reach more than 95%. And completing the engine rotor rub-impact fault diagnosis based on the wavelet-gray level co-occurrence matrix.
The invention provides a method for diagnosing faults by combining a traditional signal processing method with modern image processing, carrying out image recognition on a signal image obtained by the traditional method, extracting gray level co-occurrence matrix image features, constructing a vector feature space, and finally inputting the vector feature space into a support vector machine for fault diagnosis. Meanwhile, the feasibility of the method can be seen through simulation verification, compared with the traditional fault diagnosis, the method is obviously high in identification precision, the problems that the traditional signal processing method is incomplete in feature extraction and causes fault information loss are solved, and the problems that a neural network has high requirements on sample size and is complex and tedious in training are avoided.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present disclosure.
Claims (4)
1. The engine rotor rub-impact fault diagnosis method based on the wavelet-gray level co-occurrence matrix is characterized by comprising the following steps of:
1) collecting engine friction data;
2) performing wavelet transformation on the engine rub-impact data obtained in the step 1) to obtain a vibration signal wavelet transformation diagram;
3) converting the wavelet transformation map of the vibration signals obtained in the step 2) into a gray scale map, extracting the gray scale co-occurrence matrix image characteristic quantity of the gray scale map, and constructing a characteristic vector;
4) setting labels for the characteristic vectors obtained in the step 3), and dividing the characteristic vectors with the labels into a training set and a test set;
5) inputting the training set obtained in the step 4) into a machine learning algorithm support vector machine for training, and inputting the test set into the trained support vector machine for verification to obtain a verified support vector machine;
6) inputting the detection sample into the support vector machine verified in the step 5), diagnosing the collision and abrasion fault of the engine rotor, and outputting the result of the collision and abrasion fault diagnosis of the engine rotor.
2. The engine rotor rub-impact fault diagnosis method based on the wavelet-gray level co-occurrence matrix according to claim 1, wherein the step 2) is specifically as follows:
defining psi (t) e L2(R) satisfies the condition
Where Ψ (ω) is a Fourier transform of Ψ (t), and Ψ (t) is a basic wavelet function that is scaled and translated to obtain:
in the formula: alpha is a scale factor and tau is a translation factor;
ψα,τ(t) is a wavelet basis function depending on alpha and tau, and a group of function sequences are obtained by performing telescopic translation on a group of mother functions psi (t);
defining a presence signal x (t) e L2(R) inner-product it with the wavelet basis function, i.e.:
the continuous wavelet transform of formula (3) is x (t) and is denoted as CTW.
3. The engine rotor rub-impact fault diagnosis method based on the wavelet-gray level co-occurrence matrix according to claim 2, characterized in that:
in step 3), the image characteristic quantity comprises contrast, energy, entropy and correlation; calculating the mean value and standard deviation of the image characteristic quantities, and extracting characteristic vectors;
the contrast is reflected by the depth of the image texture grooves, as follows:
wherein p (i, j) refers to the normalized gray level co-occurrence matrix;
the energy ASM is obtained by calculating the sum of squares of element values in a gray level co-occurrence matrix:
the calculation formula of the entropy is as follows:
the formula for the correlation is as follows:
wherein
4. The engine rotor rub-impact fault diagnosis method based on the wavelet-gray level co-occurrence matrix according to claim 3, characterized in that:
in the step 4), the feature vectors are divided into a training set and a test set according to the sample division ratio of 7: 3.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114882504A (en) * | 2022-07-06 | 2022-08-09 | 成都西交智汇大数据科技有限公司 | Grading method, device, equipment and readable storage medium |
| CN117169732A (en) * | 2023-10-26 | 2023-12-05 | 宁德时代新能源科技股份有限公司 | Battery fault detection method, device, computing equipment and medium |
| CN117312930A (en) * | 2023-11-29 | 2023-12-29 | 南京邮电大学 | Transformer zero sample fault diagnosis method, device, storage medium and equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103499437A (en) * | 2013-09-11 | 2014-01-08 | 西安交通大学 | Rotary machine fault detection method of dual-tree complex wavelet transformation with adjustable quality factors |
| CN104964821A (en) * | 2015-05-22 | 2015-10-07 | 南京航空航天大学 | Fault detection method and fault detection apparatus used for shafting device |
| CN112257530A (en) * | 2020-10-13 | 2021-01-22 | 西安电子科技大学 | Rolling bearing fault diagnosis method based on blind signal separation and support vector machine |
| CN112347588A (en) * | 2020-11-26 | 2021-02-09 | 中国舰船研究设计中心 | Rotary machine fault diagnosis method based on wavelet packet decomposition |
| CN112414715A (en) * | 2020-11-05 | 2021-02-26 | 西安工程大学 | Bearing fault diagnosis method based on mixed feature and improved gray level co-occurrence algorithm |
-
2021
- 2021-06-16 CN CN202110668493.6A patent/CN113378943A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103499437A (en) * | 2013-09-11 | 2014-01-08 | 西安交通大学 | Rotary machine fault detection method of dual-tree complex wavelet transformation with adjustable quality factors |
| CN104964821A (en) * | 2015-05-22 | 2015-10-07 | 南京航空航天大学 | Fault detection method and fault detection apparatus used for shafting device |
| CN112257530A (en) * | 2020-10-13 | 2021-01-22 | 西安电子科技大学 | Rolling bearing fault diagnosis method based on blind signal separation and support vector machine |
| CN112414715A (en) * | 2020-11-05 | 2021-02-26 | 西安工程大学 | Bearing fault diagnosis method based on mixed feature and improved gray level co-occurrence algorithm |
| CN112347588A (en) * | 2020-11-26 | 2021-02-09 | 中国舰船研究设计中心 | Rotary machine fault diagnosis method based on wavelet packet decomposition |
Non-Patent Citations (3)
| Title |
|---|
| 卢娜 著: "《水电机组振动故障特征自适应提取与智能诊断方法研究》", 30 November 2019, 黄河水利出版社 * |
| 罗小荣 等: "结合小波包与神经网络的压缩机故障诊断方法", 《石油机械》 * |
| 须颖等: "基于GLCM-SDAE的滚动轴承故障诊断方法", 《沈阳建筑大学学报(自然科学版)》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114882504A (en) * | 2022-07-06 | 2022-08-09 | 成都西交智汇大数据科技有限公司 | Grading method, device, equipment and readable storage medium |
| CN117169732A (en) * | 2023-10-26 | 2023-12-05 | 宁德时代新能源科技股份有限公司 | Battery fault detection method, device, computing equipment and medium |
| CN117169732B (en) * | 2023-10-26 | 2024-04-26 | 宁德时代新能源科技股份有限公司 | Battery fault detection method, device, computing equipment and medium |
| CN117312930A (en) * | 2023-11-29 | 2023-12-29 | 南京邮电大学 | Transformer zero sample fault diagnosis method, device, storage medium and equipment |
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