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Tsinghua Science and Technology 2019, 24(6): 750-762 https://doi.org/10.26599/TST.2018.9010144
Open Access | Issue | Published: 05 December 2019

A Deep Adaptive Learning Method for Rolling Bearing Fault Diagnosis Using Immunity

Show Author's Information Yuling Tian*( ),  Xiangyu Liu
College of Information and Computer, Taiyuan University of Technology, Taiyuan 030000, China.
Keywords:
deep learning, fault diagnosis, feature extraction, clone selection strategy
Cite this article:
Tian Y, Liu X. A Deep Adaptive Learning Method for Rolling Bearing Fault Diagnosis Using Immunity. Tsinghua Science and Technology, 2019, 24(6): 750-762. https://doi.org/10.26599/TST.2018.9010144
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Abstract Full text About this article

The extraction of rolling bearing fault features using traditional diagnostic methods is not sufficiently comprehensive and the features are often chosen subjectively and depend on human experience. In this paper, an improved deep convolutional process is used to extract a set of features adaptively. The hidden multi-layer feature of deep convolutional neural networks is also exploited to improve the extraction features. A deterministic detection of low-confidence samples is performed to ensure the reliability of the recognition results and to decrease the rate of false positives by evaluating the diagnosis of the deep convolutional neural network. To improve the efficiency of the continuous learning elements of the rolling bearing fault diagnosis, a clone learning strategy based on cloning and mutation operations is proposed. The experimental results show that the proposed deep convolutional neural network model can extract multiple rolling bearing fault features, improve classification and detection accuracy by reducing the false positive rate when diagnosing rolling bearing faults, and accelerate learning efficiency when using low-confidence rolling bearing fault samples.


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About this article

A Deep Adaptive Learning Method for Rolling Bearing Fault Diagnosis Using Immunity

Show Author's information Yuling Tian*( ), Xiangyu Liu
College of Information and Computer, Taiyuan University of Technology, Taiyuan 030000, China.

Abstract

The extraction of rolling bearing fault features using traditional diagnostic methods is not sufficiently comprehensive and the features are often chosen subjectively and depend on human experience. In this paper, an improved deep convolutional process is used to extract a set of features adaptively. The hidden multi-layer feature of deep convolutional neural networks is also exploited to improve the extraction features. A deterministic detection of low-confidence samples is performed to ensure the reliability of the recognition results and to decrease the rate of false positives by evaluating the diagnosis of the deep convolutional neural network. To improve the efficiency of the continuous learning elements of the rolling bearing fault diagnosis, a clone learning strategy based on cloning and mutation operations is proposed. The experimental results show that the proposed deep convolutional neural network model can extract multiple rolling bearing fault features, improve classification and detection accuracy by reducing the false positive rate when diagnosing rolling bearing faults, and accelerate learning efficiency when using low-confidence rolling bearing fault samples.

Keywords: deep learning, fault diagnosis, feature extraction, clone selection strategy

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Publication history

Received: 27 November 2018
Revised: 09 January 2019
Accepted: 20 January 2019
Published: 05 December 2019
Issue date: December 2019

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© The author(s) 2019

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61472271).

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