CN112304611A - Deep learning-based bearing fault diagnosis method - Google Patents
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Abstract
The invention provides a bearing fault diagnosis method based on deep learning, which comprises the following steps: collecting vibration signals of bearings with different fault types, and filtering the vibration signals by utilizing wavelet denoising; s-transforming the filtered signals to obtain time-frequency spectrograms of the jth fault type, forming the time-frequency spectrograms of different fault types into a sample data set T, using the sample data set T and different fault types j as the input of a classification model, and training to obtain a bearing fault identification model; and inputting the time-frequency spectrogram of the vibration signal into a bearing fault identification model for fault identification. According to the invention, the fault characteristics of the bearing are studied in a classification mode through the CNN deep learning model, and end-to-end diagnosis of data is directly realized without manually selecting the fault characteristics.
Description
Technical Field
The invention relates to the field of bearing fault analysis, in particular to a bearing fault diagnosis method based on deep learning.
Background
The bearing is one of key parts in the rotating machinery, and intelligent fault diagnosis of the motor bearing is always a hot point of research. Early detection of emerging faults is important for current complex systems, both to save time and cost, and to facilitate taking necessary measures to avoid dangerous situations. At present, fault diagnosis methods can be divided into methods based on models, signals and expert knowledge, and these methods need historical data to establish a system fault model without a priori known model or signal mode, are difficult to establish an accurate model or extract accurate effective signal characteristics, and also have good migration capability. With the improvement of the intelligent manufacturing level, the state monitoring of electromechanical equipment advances into the 'big data' era, a large amount of production data is fully utilized, and a new opportunity is provided for a data-driven fault diagnosis method. The data hides abundant mechanical equipment operation state information. The deep learning method can directly extract the data internal relation and the complex relation between the fault characteristic quantity and the actual fault from a large amount of complex data. The end-to-end data fault diagnosis method is efficient, accurate and good in migration capability.
The prior art discloses a plunger pump fault diagnosis system based on dual-class feature fusion diagnosis, which converts a vibration signal of a pump into an electric signal through an acceleration sensor, and extracts two types of features of a wavelet packet relative energy spectrum and a wavelet packet relative feature entropy of the signal in a dual-class feature extraction mode to perform fault diagnosis. The actual operation of the system needs to connect the acceleration sensor with the pump body, and the characteristics of the vibration signals have great relation with the connection position, so that the operation at a plurality of positions of the pump body is needed to accurately monitor a certain fault, the process is complicated and time-consuming, and professional operators are needed to monitor under most conditions.
The patent in the prior art discloses a bearing early fault identification method based on a long-time and short-time memory cyclic neural network. And extracting common time domain characteristics after the bearing vibration signals are collected, constructing a characteristic data set by using the time domain characteristics and the entropy characteristics, and training the LSTM recurrent neural network by using the characteristic data set as a training sample. And identifying the fault occurrence time through the trained LSTM recurrent neural network. The method combines the traditional characteristics and entropy characteristics of the vibration signal, and accurately reflects the current state of the bearing under the condition of ensuring the physical significance of the vibration characteristic quantity. But the misjudgment caused by the interference signal can not be effectively distinguished.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a bearing fault diagnosis method based on deep learning, which decomposes fault information into a time-frequency graph containing time domain and frequency domain information through S-transform time-frequency analysis and is beneficial to observing the change of signals when a fault is initiated.
The present invention achieves the above-described object by the following technical means.
A bearing fault diagnosis method based on deep learning comprises the following steps:
acquiring vibration signals x of bearings with different fault typesj(t) denoising the vibration signal x using a waveletj(t) filtering to obtain xj(m), wherein j is a fault category;
for filtered xj(m) carrying out S transformation on the signal to obtain a time-frequency spectrogram of the jth fault type, and specifically comprising the following steps of:
for signal xj(m) performing a discrete Fourier transform to Xj[k]：
In the formula, k is a frequency point of a frequency spectrum and takes the value of a natural number of 0, 1, … and N-1; n is the number of sampling points;
x is to bej(m) and Xj[k]Performing S transformation, specifically as follows:
in the formula: t is a sampling period; n is the number of sampling points;
m is a time sampling point serial number and takes the value of a natural number of 0, 1, … and N-1;
n is the serial number of the frequency sampling point, and the value is the natural number 0, 1, …, N-1;
forming a sample data set T by using the time-frequency spectrograms of different fault types, and training the sample data set T and different fault types j to obtain a bearing fault identification model by using the sample data set T and the different fault types j as the input of a classification model;
and inputting the time-frequency spectrogram of the vibration signal into a bearing fault identification model for fault identification.
Further, the failure types include an inner ring failure, an outer ring failure, a cage failure, and a rolling body failure.
Further, the classification model adopts a Convolutional Neural Network (CNN) model, the sample data set T is divided into a training set sample and a verification set sample, the training set is used for training the Convolutional Neural Network (CNN) model, and the verification set is used for adjusting parameters of the Convolutional Neural Network (CNN) model to obtain the trained CNN model.
The invention has the beneficial effects that:
1. according to the bearing fault diagnosis method based on deep learning, fault information is decomposed into a time-frequency graph containing time domain and frequency domain information through S-transform time-frequency analysis, and therefore signal change during fault onset can be observed beneficially.
2. According to the bearing fault diagnosis method based on deep learning, the fault characteristics of the bearing are learned in a classification mode through the convolutional neural network CNN model, end-to-end data diagnosis is directly achieved, and manual selection of the fault characteristics is not needed. Compared with a shallow neural network, the CNN model has stronger learning and expression capacity on complex features, stronger classification capacity on nonlinear pattern recognition, and higher automation degree, accuracy and generalization. The method has practical application value for bearing fault identification.
Drawings
Fig. 1 is a flowchart of a deep learning-based bearing fault diagnosis method according to the present invention.
FIG. 2 is a time-frequency diagram of the present invention after S-transform, wherein a is a time-frequency diagram of cage failure; b is a rolling body fault time-frequency diagram; c is an inner ring fault time-frequency diagram; d is an outer ring fault time-frequency diagram; e is a normal state time-frequency diagram.
Fig. 3 is a diagram of a convolutional neural network CNN model according to the present invention.
FIG. 4 is a graph of the model diagnostic results according to the present invention.
Detailed Description
The invention will be further described with reference to the following figures and specific examples, but the scope of the invention is not limited thereto.
As shown in FIG. 1, the deep learning-based bearing fault end-to-end diagnosis method of the present invention includes the following steps:
acquiring vibration signals x of bearings with different fault typesj(t) denoising the vibration signal x using a waveletj(t) filtering to obtain xj(m), wherein j is a fault category;
for filtered xj(m) carrying out S transformation on the signals to obtain a time-frequency diagram of the jth fault type;
and (3) forming a sample data set T by using the time-frequency graphs of different fault types, and training to obtain a bearing fault identification model by using the sample data set T and different fault types j as the input of a classification model. The classification model adopts a Convolutional Neural Network (CNN) model. And dividing the sample data set T into a training set sample and a verification set sample, training the convolutional neural network CNN model by using the training set, and adjusting parameters of the network model by using the verification set to obtain the trained convolutional neural network CNN model.
And judging the fault category of the rolling bearing by using the trained classification model. And processing the vibration signals to be classified according to the same steps and methods during training to obtain a time-frequency diagram, inputting the time-frequency diagram into a trained Convolutional Neural Network (CNN) model, and giving corresponding fault categories by the Convolutional Neural Network (CNN) model.
Example (b):
the motor driving end bearing is a deep groove ball bearing with the model number of 6324C3, the number of rollers N is 12, the inner diameter D is 120mm, the outer diameter D is 260mm, and the contact angle beta is 0 deg. The sampling frequency is 2000Hz, and the motor speed is 1450 r/min. Respectively collecting vibration signals x for different faults of bearingj(t), j indicates the type of fault, where different faults include: normal state, cage failure, rolling element failure, outer ring failure, inner ring failure. x is the number of1(t) is a normal state, x2(t) cage failure, x3(t) failure of rolling elements, x4(t) outer ring failure and x5(t) inner ring failure.
Next, a detailed description will be made regarding a bearing inner race failure.
For the collected bearing inner ring fault signal x5(t) performing wavelet de-noising to obtain x5(m)。
For signal x5(m) performing a discrete Fourier transform to X5[k]：
In the formula, k is a frequency point of a frequency spectrum and takes the value of a natural number of 0, 1, … and N-1; n is the number of sampling points;
x is to be5(m) and X5[k]Performing S transformation, specifically as follows:
in the formula: t is a sampling period; n is the number of sampling points;
m is a time sampling point serial number and takes the value of a natural number of 0, 1, … and N-1;
n is the serial number of the frequency sampling point, and the value is the natural number 0, 1, …, N-1;
And converting the obtained feature vectors of the time domain and the frequency domain of the 5 th fault type into a gray-scale time-frequency diagram, and outputting the gray-scale time-frequency diagram as shown in fig. 2.
And (3) performing the steps on the data of normal state, retainer fault, rolling element fault and outer ring fault, and forming a sample data set T by using the time-frequency graphs of 5 different fault types.
And training the sample data set T and the corresponding fault category as the input of a Convolutional Neural Network (CNN) model. The total number of samples used for each type of fault is 500, the number of training samples is 400, and the number of testing samples is 100. The convolutional neural network CNN model is shown in fig. 3. The embodiment is based on MATLAB software platform for training and recognition.
Wherein, each parameter of the convolutional neural network CNN model is shown in table 1:
TABLE 1
Wherein, the batch of each training is 100, the iteration times is 3000, and the learning rate is 0.0001.
And inputting the time-frequency graph extracted from the test set sample into the trained convolutional neural network CNN model, wherein the convolutional neural network CNN model can give out corresponding fault categories. The test results are shown in fig. 4.
And constructing bearing fault recognition models of different models, and forming a bearing fault database.
And identifying the bearing vibration signal of unknown fault by using the bearing fault identification model, and judging the fault type.
The present invention is not limited to the above-described embodiments, and any obvious improvements, substitutions or modifications can be made by those skilled in the art without departing from the spirit of the present invention.
Claims (3)
1. A bearing fault diagnosis method based on deep learning is characterized by comprising the following steps:
acquiring vibration signals x of bearings with different fault typesj(t) denoising the vibration signal x using a waveletj(t) filtering to obtain xj(m), wherein j is a fault category;
for filtered xj(m) carrying out S transformation on the signal to obtain a time-frequency spectrogram of the jth fault type, and specifically comprising the following steps of:
for signal xj(m) performing a discrete Fourier transform to Xj[k]：
In the formula, k is a frequency point of a frequency spectrum and takes the value of a natural number of 0, 1, … and N-1; n is the number of sampling points;
x is to bej(m) and Xj[k]Performing S transformation, specifically as follows:
in the formula: t is a sampling period; n is the number of sampling points;
m is a time sampling point serial number and takes the value of a natural number of 0, 1, … and N-1;
n is the serial number of the frequency sampling point, and the value is the natural number 0, 1, …, N-1;
forming a sample data set T by using the time-frequency spectrograms of different fault types, and training the sample data set T and different fault types j to obtain a bearing fault identification model by using the sample data set T and the different fault types j as the input of a classification model;
and inputting the time-frequency spectrogram of the vibration signal into a bearing fault identification model for fault identification.
2. The deep learning-based bearing failure diagnosis method according to claim 1, wherein the failure categories include an inner ring failure, an outer ring failure, a cage failure, and a rolling element failure.
3. The deep learning-based bearing fault diagnosis method according to claim 1, wherein the classification model adopts a Convolutional Neural Network (CNN) model, the sample data set T is divided into training set samples and validation set samples, the Convolutional Neural Network (CNN) model is trained by using the training set, and the Convolutional Neural Network (CNN) model is parametrized by using the validation set, so as to obtain the trained CNN model.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113865872A (en) * | 2021-11-03 | 2021-12-31 | 西安电子科技大学 | Bearing fault diagnosis method based on wavelet packet reconstruction imaging and CNN |
| CN114088401A (en) * | 2021-11-03 | 2022-02-25 | 宁波坤博测控科技有限公司 | Fault analysis method and device for rolling bearing of wind driven generator |
| CN114593917A (en) * | 2022-03-08 | 2022-06-07 | 安徽理工大学 | Small sample bearing fault diagnosis method based on triple model |
| CN115030903A (en) * | 2022-06-16 | 2022-09-09 | 江苏大学镇江流体工程装备技术研究院 | On-line diagnosis method for early fault of rolling bearing in centrifugal pump |
| CN115358277A (en) * | 2022-10-09 | 2022-11-18 | 深圳市信润富联数字科技有限公司 | Bearing fault diagnosis method, device, equipment and readable storage medium |
| CN117249996A (en) * | 2023-11-10 | 2023-12-19 | 太原理工大学 | Fault diagnosis method for gearbox bearing of mining scraper |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113865872A (en) * | 2021-11-03 | 2021-12-31 | 西安电子科技大学 | Bearing fault diagnosis method based on wavelet packet reconstruction imaging and CNN |
| CN114088401A (en) * | 2021-11-03 | 2022-02-25 | 宁波坤博测控科技有限公司 | Fault analysis method and device for rolling bearing of wind driven generator |
| CN113865872B (en) * | 2021-11-03 | 2023-07-28 | 西安电子科技大学 | Bearing fault diagnosis method based on wavelet packet reconstruction imaging and CNN |
| CN114593917A (en) * | 2022-03-08 | 2022-06-07 | 安徽理工大学 | Small sample bearing fault diagnosis method based on triple model |
| CN115030903A (en) * | 2022-06-16 | 2022-09-09 | 江苏大学镇江流体工程装备技术研究院 | On-line diagnosis method for early fault of rolling bearing in centrifugal pump |
| CN115030903B (en) * | 2022-06-16 | 2023-07-07 | 江苏大学镇江流体工程装备技术研究院 | Online diagnosis method for early faults of rolling bearings in centrifugal pump |
| CN115358277A (en) * | 2022-10-09 | 2022-11-18 | 深圳市信润富联数字科技有限公司 | Bearing fault diagnosis method, device, equipment and readable storage medium |
| CN117249996A (en) * | 2023-11-10 | 2023-12-19 | 太原理工大学 | Fault diagnosis method for gearbox bearing of mining scraper |
| CN117249996B (en) * | 2023-11-10 | 2024-02-13 | 太原理工大学 | Fault diagnosis method for gearbox bearing of mining scraper |
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