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Rolling bearing fault analysis based on CNN and LSTM

A rolling bearing and fault classification technology, applied in the testing of mechanical components, character and pattern recognition, pattern recognition in signals, etc., can solve problems such as gradient disappearance

Pending Publication Date: 2021-03-16
BEIJING TECHNOLOGY AND BUSINESS UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Moreover, compared with the traditional RNN, LSTM solves the problem of gradient disappearance and can reduce the difficulty of training the model.

Method used

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  • Rolling bearing fault analysis based on CNN and LSTM
  • Rolling bearing fault analysis based on CNN and LSTM
  • Rolling bearing fault analysis based on CNN and LSTM

Examples

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Embodiment Construction

[0040] figure 1 It is a schematic flowchart of a rolling bearing fault analysis method based on CNN and LSTM according to an embodiment of the present application. see figure 1 It is known that a CNN and LSTM-based rolling bearing fault analysis method and system provided in the embodiment of the present application may include:

[0041] Step S1: Denoise the processed vibration data using a Butterworth Filter, and perform Fast Fourier Transform (FFT) to convert the preprocessed time-domain signal into a frequency-domain signal;

[0042] Step S2: using CNN network for learning to obtain the image features of the time domain map and the frequency domain map;

[0043] Step S3: performing image feature fusion through the add layer;

[0044]Step S4: Input the fused image features obtained by the add layer into the LSTM network, and further learn the time series features contained in the features through the LSTM network;

[0045] Step S5: realize the classification function thr...

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Abstract

The invention discloses a rolling bearing fault classification method based on CNN and LSTM. The method comprises the steps: acquiring and preprocessing bearing vibration data, and constructing a dataset; denoising the processed vibration data by using a Butterworth Filter, and carrying out fast Fourier transform (FFT) to convert the preprocessed time domain signal into a frequency domain signal;employing a CNN network for learning to obtain image features of a time domain graph and a frequency domain graph, and carrying out image feature fusion through an Add layer; inputting image featuresobtained by CNN learning into an LSTM network, and realizing a classification function through a full connection layer and a Softmax function according to time sequence features contained in the LSTMnetwork learning features. The trained network is used for fault classification of test samples, and the method has important practical significance and practical value for early detection and analysis of weak faults of the rolling bearing.

Description

technical field [0001] The application belongs to the technical field of machine learning and fault identification, and in particular relates to a rolling bearing fault analysis method of a deep learning model CNN and a deep learning model LSTM. Background technique [0002] Rolling bearing fault diagnosis technology has always been an important research topic in the field of mechanical fault diagnosis. As a complex mechanical equipment, trains are widely used in public travel. Failure of the train mechanical system will interfere with the operation of the transportation system, and in serious cases will endanger the safety of personnel and cause huge economic losses. Rolling bearings are the core components of trains. According to statistics, 30% of mechanical equipment failures are caused by bearing failures, and the early detection and analysis of weak rolling bearing failures has important practical significance and practical value. Different fault types have differen...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08G01M13/045
CPCG06N3/08G01M13/045G06N3/044G06N3/045G06F2218/04G06F2218/08G06F2218/12G06F18/213G06F18/253
Inventor 刘瑞军章博华王俊张伦
Owner BEIJING TECHNOLOGY AND BUSINESS UNIVERSITY
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