阅读说明：本技术 一种基于深度学习的旋转机械齿轮箱故障诊断方法 (Rotary machine gear box fault diagnosis method based on deep learning ) 是由 张能文 杨凯铭 秦法涛 何晓琳 吴广 崔飞 蔡昌春 江冰 承敏钢 于 2021-09-02 设计创作，主要内容包括：本发明的一种基于深度学习的旋转机械齿轮箱故障诊断方法,属于旋转机械装备故障类型识别和诊断技术领域,包括采集旋转机械齿轮箱运行振动加速度、速度、位移、频率等信号,基于奇异值分解的振动信号降噪实现原始信号重构,利用对称点阵图像分析法将振动信号的一维时间序列形式映射至二维空间中构建二维图像信息,基于离散点的密集程度进行故障类型特征提取；最后,结合对称点阵图像分析构建的二维图像信息和深度神经网络进行故障训练和判别,获得故障诊断模型,本发明能够准确有效的对直齿轮箱内部的齿轮与轴承进行故障诊断和故障识别。(The invention discloses a rotary machine gear box fault diagnosis method based on deep learning, which belongs to the technical field of rotary machine equipment fault type identification and diagnosis, and comprises the steps of collecting signals such as running vibration acceleration, speed, displacement, frequency and the like of a rotary machine gear box, denoising vibration signals based on singular value decomposition to realize original signal reconstruction, mapping a one-dimensional time sequence form of the vibration signals to a two-dimensional space by using a symmetric dot matrix image analysis method to construct two-dimensional image information, and extracting fault type characteristics based on the density degree of discrete points; and finally, fault training and discrimination are carried out by combining two-dimensional image information and a deep neural network which are constructed by analyzing the symmetrical dot matrix image to obtain a fault diagnosis model.)

1. A rotary machine gearbox fault diagnosis method based on deep learning is characterized by comprising the following steps:

s1, collecting a vibration signal of the operation of the rotary mechanical gear box;

s2, carrying out singular value decomposition on the vibration signal to obtain an original signal;

s3, mapping the one-dimensional time sequence form of the original signal to a two-dimensional space by using a symmetric dot matrix image analysis method to construct two-dimensional image information;

s4, extracting fault type features according to the density degree of discrete points of the two-dimensional image information;

and S5, carrying out fault training and discrimination by using the extracted fault type to carry out deep neural network.

2. The method of claim 1, wherein the vibration signals include rotational acceleration, rotational speed, displacement, and frequency.

3. The rotating machine gearbox fault diagnosis method based on deep learning of claim 1, wherein the singular value decomposition method in the step S2 is as follows:

s21, constructing a one-dimensional characteristic matrix Hankel matrix of the vibration acceleration time sequence signal, setting an original noise-containing signal sequence as X ═ X (1), X (2), … X (N), and constructing the Hankel matrix as follows:

wherein q < N and satisfies N ═ p + q-1, A_p×qIs a true signal feature matrix in the original time sequence signal, B_p×qAnd when constructing a Hankel matrix, the number p of rows is equal to the number q of columns, the time sequence of even data points is taken, and even processing is carried out if the sequence is an odd number.

S22, carrying out singular value decomposition on the Hankel matrix, and expressing the Hankel matrix as the product of two orthogonal matrices and an asymmetric diagonal matrix:

wherein the content of the first and second substances,and V_q×qP-order and q-order orthogonal matrixes respectively; m_p×qIs an asymmetric diagonal matrix;

S＝diag(σ₁,σ₂,…,σ_r)，

σ_iis the singular value of the matrix Hankel, and r is the rank of the matrix.

S23, selecting L effective singular values, setting the rest singular values as 0, and reconstructing an inverse matrix in the singular value decomposition process to obtain a reconstructed matrix H ', wherein the H' is a Hankel matrix of the denoised vibration signal, and the method comprises the following steps:

s24, carrying out inverse singular decomposition process on the H' matrix to obtain the vibration acceleration signal of the gearbox after noise reduction

X′＝[x′(1),x′(2),…x′(N)]。

4. The rotating machinery gearbox fault diagnosis method based on deep learning of claim 3, wherein the determination of the L value in the step S23 adopts an inverse singular value average spectrum method, and the inverse singular value average spectrum method arranges the obtained singular values in a big-to-small manner:

K＝rank(σ₁,σ₂,…,σ_m)

definition ofAnd comparing the b with all singular values, considering the singular value larger than the mean value as an effective singular value, setting other singular values as 0, and performing signal reconstruction.

5. The rotating machine gearbox fault diagnosis method based on deep learning of claim 1, wherein: the specific content of step S3 is to map the one-dimensional time-series vibration deceleration signal after noise reduction into a two-dimensional space by using a symmetric image lattice analysis method, form a related symmetric pattern in the two-dimensional space, and perform feature extraction on the gear vibration acceleration signal of the rotating equipment by using the petal signal density, where the vibration acceleration values corresponding to the i-th and i + l-th signals of the time series are x '(i) and x' (i + l), the signals at these two times are mapped into the two-dimensional space by using a symmetric lattice transformation, and the polar coordinates are represented as: z (r (i), θ (i), φ (i)), the mapping equation is as follows:

wherein r is the radius of the polar coordinate pattern, and theta is the angle of the petals deflected along the mirror image of the symmetrical plane in the counterclockwise direction; phi is a mirror image of the petals in the opposite direction clockwise; x is the number of_maxIs the maximum amplitude in the signal sequence; x is the number of_minIs the minimum amplitude in the signal sequence; l is a time interval parameter; theta is a corresponding symmetrical angle of the petals when the petals are turned in a mirror image manner,to enlarge factor

6. The rotating machine gearbox fault diagnosis method based on deep learning of claim 5, wherein: the above-mentionedThe selection of the values l and theta is optimized by improving the particle swarm optimization, an optimization model with the two-dimensional image discrimination index as a target function is constructed, and optimization is carried outl and theta, and the distinguishing indexes comprise: the thickness difference of the petal arms of the two-dimensional patterns and the included angle of the quasi-regression sidelines of the two adjacent petal arms are represented, and the objective function is represented by the functional of the corresponding points as follows:

wherein, | | | is functional calculation, and N is the number of sequences.

7. The rotating machine gearbox fault diagnosis method based on deep learning of claim 6, wherein: according to the improved particle swarm algorithm, the influence of the average fitness position of the particle swarm on the position of the next generation of particles is increased in a particle swarm position formula, so that the convergence speed of the system is improved, and the position formula is as follows:

v_i(k)＝ωv_i(k-1)+c₁r₁[p_i,best(k-1)-x_i(k-1)]+c₂r₂[g_best-x_i(k-1)]+c₃r₃[x_avg(k-1)-x_i(k-1)]

x_i(k)＝v_i(k)+x_i(k-1)

where ω is the inertial weight of the flight velocity, v_iIs the particle velocity, x_iIs the position of the particle, x_avgIs the average position of the particles; r is₁，r₂，r₃，c₁，c₁，c₃The values of the random numbers are (0-1), (0-2) and (0-2), and k is the number of iterations.

8. The rotating machine gearbox fault diagnosis method based on deep learning of claim 1, wherein: in step S5, a neural network training model is constructed progressively from the optimization of four main parameters, such as the iteration number, batch size, convolution kernel coefficient, and the like of the neural network, for the vibration signal characteristics of the rotating machine gearbox.

9. The rotating machinery gearbox fault diagnosis method based on deep learning of claim 8, wherein parameters such as iteration number, batch size, convolution kernel size and convolution kernel number of a neural network are optimized by using network calculation, wherein the network calculation is as follows:

setting the actual output O of the network, and the expected outputs Y and E as the difference, wherein k is iteration number, omega is batch size, b is convolution kernel size, beta convolution kernel number, and k, omega, b and beta satisfy the following relations:

wherein eta is₁，η₂，η₃，η₄The step size adaptive adjustment variables are random numbers of (0, 1).

10. The rotating machine gearbox fault diagnosis method based on deep learning of claim 1, wherein: the failure types include, but are not limited to, rotating machine gearbox gear missing teeth, broken teeth, wear, root cracks, bearing inner race failures, outer race, balls, hybrid failures, and the like.

Technical Field

The invention belongs to the technical field of fault type identification and diagnosis of rotary mechanical equipment, and particularly relates to a fault diagnosis method of a rotary mechanical gearbox based on deep learning.

Background

The rotating machinery is an important device for realizing energy conversion, is used in a large amount in the industrial field, has important relation between the reliability and the safety of operation, directly influences industrial production due to equipment faults, and even threatens the personal safety of workers at the same time, so that the strengthening of the detection of the running state of the rotating machinery equipment and the identification of fault types is very important. With the development of deep learning and big data artificial intelligence technology, a technical support is provided for fault diagnosis of mechanical equipment, how to judge the running state and the development trend of the rotary equipment by using the artificial intelligence technology, analyze the fault reason, the position and the property of the rotary equipment and provide scientific inspection basis for industrial production, thereby effectively reducing the maintenance and the maintenance cost of the equipment, improving the economical efficiency of the equipment operation and ensuring the safe production.

Disclosure of Invention

1. Technical problem to be solved by the invention

The object of the present invention is to solve the above mentioned drawbacks.

2. Technical scheme

In order to achieve the purpose, the technical scheme provided by the invention is as follows:

the invention discloses a rotary machine gearbox fault diagnosis method based on deep learning, which comprises the following steps:

s1, collecting a vibration signal of the operation of the rotary mechanical gear box;

s2, carrying out singular value decomposition on the vibration signal to obtain an original signal;

s3, mapping the one-dimensional time sequence form of the original signal to a two-dimensional space by using a symmetric dot matrix image analysis method to construct two-dimensional image information;

s4, extracting fault type features according to the density degree of discrete points of the two-dimensional image information;

and S5, carrying out fault training and discrimination by using the extracted fault type to carry out deep neural network.

Preferably, the vibration signal includes rotational acceleration, rotational speed, displacement, and frequency.

Preferably, the singular value decomposition method in step S2 is as follows:

s21, constructing a one-dimensional characteristic matrix Hankel matrix of the vibration acceleration time sequence signal, setting an original noise-containing signal sequence as X ═ X (1), X (2),. X (N), and constructing the Hankel matrix as follows:

wherein q < N and satisfies N ═ p + q-1, A_p×qIs a true signal feature matrix in the original time sequence signal, B_p×qConstructing Hankel moment for characteristic matrix formed by noise matrix sequenceAnd in the process of array, the number p of rows is equal to the number q of columns, the time sequence of even data points is taken, and even processing is carried out if the sequence is an odd number.

S22, carrying out singular value decomposition on the Hankel matrix, and expressing the Hankel matrix as the product of two orthogonal matrices and an asymmetric diagonal matrix:

wherein the content of the first and second substances,and V_q×qP-order and q-order orthogonal matrixes respectively; m_p×qIs an asymmetric diagonal matrix;

σ_iis the singular value of the matrix Hankel, and r is the rank of the matrix.

S23, selecting L effective singular values, setting the rest singular values as 0, and reconstructing an inverse matrix in the singular value decomposition process to obtain a reconstructed matrix H ', wherein the H' is a Hankel matrix of the denoised vibration signal, and the method comprises the following steps:

s24, carrying out inverse singular decomposition process on the H' matrix to obtain the vibration acceleration signal of the gearbox after noise reduction

X′＝[x′(1)，x′(2)，...x′(N)]。

Preferably, in step S23, the determination of the L value is performed by using an inverse singular value averaging method, where the obtained singular values are arranged in a descending manner:

K＝rank(σ₁，σ₂，...，σ_m)

definition ofAnd comparing the b with all singular values, considering the singular value larger than the mean value as an effective singular value, setting other singular values as 0, and performing signal reconstruction.

Preferably, the specific content of step S3 is to map the one-dimensional time-series vibration deceleration signal after noise reduction into a two-dimensional space by using a symmetric image lattice analysis method, form a related symmetric pattern in the two-dimensional space, and perform feature extraction of the gear vibration acceleration signal of the rotating equipment by using the petal signal density, where the vibration acceleration values corresponding to the time-series i and i + l signals are x '(i) and x' (i + l), the signals at these two times are mapped into the two-dimensional space by using a symmetric lattice transformation, and the polar coordinates are expressed as: z (r (i), θ (i), φ (i)), the mapping equation is as follows:

wherein r is the radius of the polar coordinate pattern, and theta is the angle of the petals deflected along the mirror image of the symmetrical plane in the counterclockwise direction; phi is a mirror image of the petals in the opposite direction clockwise; x is the number of_maxIs the maximum amplitude in the signal sequence; x is the number of_minIs the minimum amplitude in the signal sequence; l is a time interval parameter; theta is a corresponding symmetrical angle of the petals when the petals are turned in a mirror image manner,to enlarge factor

Preferably, the first and second liquid crystal materials are,the selection of the values l and theta is optimized by improving the particle swarm optimization, an optimization model with the two-dimensional image discrimination index as a target function is constructed, and optimization is carried outl and theta, and the distinguishing indexes comprise: the thickness difference of the petal arms of the two-dimensional patterns and the included angle of the quasi-regression sidelines of the two adjacent petal arms are represented, and the objective function is represented by the functional of the corresponding points as follows:

wherein, | | | is functional calculation, and N is the number of sequences.

Preferably, the particle swarm algorithm is improved, and the influence of the average fitness position of the particle swarm on the position of the next generation of particles is increased in the particle swarm position formula, so that the convergence speed of the system is improved, wherein the position formula is as follows:

v_i(k)＝ωv_i(k-1)+c₁r₁[p_i，best(k-1)-x_i(k-1)]

+c₂r₂[g_best-x_i(k-1)]+c₃r₃[x_avg(k-1)-x_i(k-1)]

x_i(k)＝v_i(k)+x_i(k-1)

where ω is the inertial weight of the flight velocity, v_iIs the particle velocity, x_iIs the position of the particle, x_avgIs the average position of the particles; r is₁，r₂，r₃，c₁，c₁，c₃The values of the random numbers are (0-1), (0-2) and (0-2), and k is the number of iterations.

Preferably, in step S5, a neural network training model is constructed from optimization of four main parameters, i.e., the iteration number, batch size, convolution kernel size, and convolution kernel coefficient of the neural network, according to the vibration signal characteristics of the rotating machine gearbox.

Preferably, the network calculation is used for optimizing parameters such as the iteration number, the batch size, the convolution kernel size, the number of convolution kernels and the like of the neural network, and the network calculation is as follows:

setting the actual output O of the network, and the expected outputs Y and E as the difference, wherein k is iteration number, omega is batch size, b is convolution kernel size, beta convolution kernel number, and k, omega, b and beta satisfy the following relations:

wherein eta is₁，η₂，η₃，η₄The step size adaptive adjustment variables are random numbers of (0, 1).

Preferably, the failure types include, but are not limited to, rotating machine gearbox gear missing teeth, broken teeth, wear, tooth root cracking, bearing inner race failure, outer race, balls, mixed failures, and the like.

3. Advantageous effects

Compared with the prior art, the technical scheme provided by the invention has the following beneficial effects:

(1) according to the rotating machinery gearbox fault diagnosis method based on deep learning, a fault diagnosis model can be obtained, and then fault diagnosis and fault recognition can be accurately and effectively carried out on gears and bearings in a direct gearbox.

Drawings

FIG. 1 is a flow chart of a method for deep learning based fault diagnosis of a rotating machine gearbox in accordance with the present invention;

FIG. 2 is a noise reduction effect diagram of a rotating machine gearbox fault diagnosis method based on deep learning according to the present invention;

FIG. 3 is a diagram of a gearbox fault-free signal collected by a deep learning-based rotating machine gearbox fault diagnosis method of the present invention;

FIG. 4 is a plot of gear box root crack signals collected for a deep learning based rotating machine gear box fault diagnostic method of the present invention;

FIG. 5 is a diagram of collected gearbox gear wear signals for a deep learning based rotating machine gearbox fault diagnosis method of the present invention;

FIG. 6 is a diagram of gear-box gear tooth-breaking signals collected by the rotating machinery gear-box fault diagnosis method based on deep learning of the present invention;

FIG. 7 is a diagram of collected gear missing signals of a gearbox according to a method for diagnosing faults of a rotary mechanical gearbox based on deep learning according to the present invention;

FIG. 8 is a two-dimensional map of a rotating machine gearbox fault diagnosis method based on deep learning according to the present invention under normal conditions of the gearbox;

FIG. 9 is a two-dimensional map of missing gear of a gear box according to a method for diagnosing faults of a rotary mechanical gear box based on deep learning according to the present invention;

FIG. 10 is a two-dimensional map of a gear box gear tooth breakage of a rotary mechanical gear box fault diagnosis method based on deep learning according to the present invention;

FIG. 11 is a two-dimensional map of gear box gear wear for a rotating machine gear box fault diagnosis method based on deep learning of the present invention;

FIG. 12 is a two-dimensional map of gearbox root cracking under a rotary machine gearbox fault diagnosis method based on deep learning of the present invention.

Detailed Description

In order to facilitate an understanding of the invention, the invention will now be described more fully hereinafter with reference to the accompanying drawings, in which several embodiments of the invention are shown, but which may be embodied in many different forms and are not limited to the embodiments described herein, but rather are provided for the purpose of providing a more thorough disclosure of the invention.

It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present; when an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present; the terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs; the terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention; as used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

Example 1

Referring to fig. 1, the method for diagnosing faults of a rotary machine gearbox based on deep learning of the embodiment includes the following steps:

s1, collecting a vibration signal of the operation of the rotary mechanical gear box;

s2, carrying out singular value decomposition on the vibration signal to obtain an original signal;

s3, mapping the one-dimensional time sequence form of the original signal to a two-dimensional space by using a symmetric dot matrix image analysis method to construct two-dimensional image information;

s4, extracting fault type features according to the density degree of discrete points of the two-dimensional image information;

and S5, carrying out fault training and discrimination by using the extracted fault type to carry out deep neural network.

The vibration signal of the present embodiment includes rotational acceleration, rotational speed, displacement, and frequency.

The singular value decomposition method in step S2 of the present embodiment is as follows:

s21, constructing a one-dimensional characteristic matrix Hankel matrix of the vibration acceleration time sequence signal, setting an original noise-containing signal sequence as X ═ X (1), X (2),. X (N), and constructing the Hankel matrix as follows:

wherein q < N and satisfies N ═ p + q-1, A_p×qIs a true signal feature matrix in the original time sequence signal, B_p×qAnd when constructing a Hankel matrix, the number p of rows is equal to the number q of columns, the time sequence of even data points is taken, and even processing is carried out if the sequence is an odd number.

S22, carrying out singular value decomposition on the Hankel matrix, and expressing the Hankel matrix as the product of two orthogonal matrices and an asymmetric diagonal matrix:

wherein the content of the first and second substances,and V_q×qP-order and q-order orthogonal matrixes respectively; m_p×qIs an asymmetric diagonal matrix;

σ_iis the singular value of the matrix Hankel, and r is the rank of the matrix.

S23, selecting L effective singular values, setting the rest singular values as 0, and reconstructing an inverse matrix in the singular value decomposition process to obtain a reconstructed matrix H ', wherein the H' is a Hankel matrix of the denoised vibration signal, and the method comprises the following steps:

s24, carrying out inverse singular decomposition process on the H' matrix to obtain the vibration acceleration signal of the gearbox after noise reduction

X′＝[x′(1)，x′(2)，...x′(N)]。

In step S23 of this embodiment, an inverse singular value averaging method is used to determine the L value, and the obtained singular values are arranged in a descending manner by the inverse singular value averaging method:

K＝rank(σ₁，σ₂，...，σ_m)

definition ofAnd comparing the b with all singular values, considering the singular value larger than the mean value as an effective singular value, setting other singular values as 0, and performing signal reconstruction.

The specific content of step S3 in this embodiment is to map the one-dimensional time-series vibration deceleration signal after noise reduction into a two-dimensional space by using a symmetric image lattice analysis method, form a related symmetric pattern in the two-dimensional space, and perform feature extraction of the gear vibration acceleration signal of the rotating equipment by using the petal signal density, where the vibration acceleration values corresponding to the i-th and i + l-th time-series signals are x '(i) and x' (i + l), the signals at these two times are mapped into the two-dimensional space by using a symmetric lattice transformation, and the polar coordinates are represented as: z (r (i), θ (i), φ (i)), the mapping equation is as follows:

wherein r is the radius of the polar coordinate pattern, and theta is the angle of the petals deflected along the mirror image of the symmetrical plane in the counterclockwise direction; phi is a mirror image of the petals in the opposite direction clockwise; x is the number of_maxIs the maximum amplitude in the signal sequence; x is the number of_minIs the minimum amplitude in the signal sequence; l is a time interval parameter; theta is a corresponding symmetrical angle of the petals when the petals are turned in a mirror image manner,to enlarge factor

Of the present embodimentThe selection of the values l and theta is optimized by improving the particle swarm optimization, an optimization model with the two-dimensional image discrimination index as a target function is constructed, and optimization is carried outl and theta, and the distinguishing indexes comprise: the thickness difference of the petal arms of the two-dimensional patterns and the included angle of the quasi-regression sidelines of the two adjacent petal arms are represented, and the objective function is represented by the functional of the corresponding points as follows:

wherein, | | | is functional calculation, and N is the number of sequences.

In the improved particle swarm algorithm of this embodiment, the influence of the average fitness position of the particle swarm on the position of the next generation of particles is added to the particle swarm position formula, so as to improve the convergence rate of the system, where the position formula is as follows:

v_i(k)＝ωv_i(k-1)+c₁r₁[p_i，best(k-1)-x_i(k-1)]

+c₂r₂[g_best-x_i(k-1)]+c₃r₃[x_avg(k-1)-x_i(k-1)]

x_i(k)＝v_i(k)+x_i(k-1)

where ω is the inertial weight of the flight velocity, v_iIs the particle velocity, x_iIs the position of the particle, x_avgIs the average position of the particles; r is₁，r₂，r₃，c₁，c₁，c₃The values of the random numbers are (0-1), (0-2) and (0-2), and k is the number of iterations.

In step S5 of this embodiment, a neural network training model is constructed from optimization of four main parameters, such as the number of iterations of the neural network, the batch size, the convolution kernel size, and the convolution kernel coefficient, for the vibration signal characteristics of the rotating machine gearbox.

In this embodiment, parameters such as the number of iterations, the batch size, the size of convolution kernels, and the number of convolution kernels of the neural network are optimized by using network calculation, and the network calculation is as follows:

setting the actual output O of the network, and the expected outputs Y and E as the difference, wherein k is iteration number, omega is batch size, b is convolution kernel size, beta convolution kernel number, and k, omega, b and beta satisfy the following relations:

wherein eta is₁，η₂，η₃，η₄The step size adaptive adjustment variables are random numbers of (0, 1).

The failure types of the present embodiments include, but are not limited to, rotating machine gearbox gear missing teeth, broken teeth, wear, tooth root cracking, bearing inner race failure, outer race, ball, hybrid failure, and the like.

As shown in fig. 2, noise reduction is performed when the singular value is 2, and the effect is as shown in the figure.

Fig. 3-7 show the waveform diagrams of the vibration signals collected under different conditions, which are normal, tooth root crack, gear wear, gear breakage and gear missing in sequence.

Fig. 8-12 show, respectively, two-dimensional maps of vibration signals acquired under different conditions, in order normal, gear missing, gear broken, gear wear, and tooth root cracking.

The vibration signal without noise reduction processing is as follows:

TABLE 1

Test specimen	Is normal	Failure 1	Failure 2	Failure 3	Failure 4
						Sample 1	0.7877	0.7511	0.7466	0.7321	0.7751
Sample 2	0.8332	0.8589	0.7467	0.6533	0.7132
						Sample 3	0.7107	0.7531	0.8158	0.7667	0.8042
Sample 4	0.7752	0.8443	0.6514	0.8933	0.8911
						Sample 5	0.7423	0.8048	0.8788	0.8689	0.8992

The vibration signals subjected to the noise reduction processing in table 1 are as follows:

TABLE 2

The above-mentioned embodiments only express a certain implementation mode of the present invention, and the description thereof is specific and detailed, but not construed as limiting the scope of the present invention; it should be noted that, for those skilled in the art, without departing from the concept of the present invention, several variations and modifications can be made, which are within the protection scope of the present invention; therefore, the protection scope of the present patent shall be subject to the appended claims.