An early warning method for mechanical fault detection based on adversarial auto-encoders

Jijie HOU; Bo MA; Libing LIANG; Ming ZHANG

doi:10.51393/j.jamst.2022006

Volume 2 Issue 2

Mar. 2022

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Jijie HOU, Bo MA, Libing LIANG, Ming ZHANG. An early warning method for mechanical fault detection based on adversarial auto-encoders[J]. Journal of Advanced Manufacturing Science and Technology , 2022, 2(2): 2022006. doi: 10.51393/j.jamst.2022006

Citation:

Jijie HOU, Bo MA, Libing LIANG, Ming ZHANG. An early warning method for mechanical fault detection based on adversarial auto-encoders[J]. Journal of Advanced Manufacturing Science and Technology , 2022, 2(2): 2022006. doi: 10.51393/j.jamst.2022006

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An early warning method for mechanical fault detection based on adversarial auto-encoders

doi: 10.51393/j.jamst.2022006

Jijie HOU^a,b,
Bo MA^{a,b
,
,},
Libing LIANG^c,
Ming ZHANG^d

a School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
b Beijing Key Laboratory of Health Monitoring control and Fault Self-Recovery for High-end Mechanical Equipment, Beijing University of Chemical Technology, Beijing 100029, China
c China United Network Communications Co., Ltd. Guangzhou Branch, Guangzhou 510630, China
d College of Engineering and Physical Sciences Aston Triangle, Birmingham B47ET, UK

Received Date: 2022-01-02
Accepted Date: 2022-03-01
Rev Recd Date: 2022-02-10

Available Online: 2022-03-15

Publish Date: 2022-03-14

Abstract

Abstract

The vibration signal of mechanical equipment is non-linear and non-stationary, and it is difficult to fully reflect the operation state of equipment through traditional fixed threshold alarm method. While the early warning method based on multi-feature parameter fusion relies on manual experience to extract features, which is difficult to ensure the accuracy of the extracted features and cannot achieved good early warning effect. To solve this problem, a feature self-learning method based on adversarial auto-encoders is proposed in this paper, which encodes high-dimensional monitoring data in normal state into low-dimensional vectors with certain statistical laws and uses it as a benchmark to detect abnormalities in the operating state of the equipment in time by measuring the difference between the encoded features of real-time monitoring data and the benchmark. The actual application cases of reciprocating compressors show that the proposed method can detect the weak signs of equipment fault at the early stage, and realize early warning. At the same time, by comparing with the Auto-Encoders network-based warning method and the Dirichlet process mixture model-based warning method, It is verified that the method in this paper has more advantages in terms of warning accuracy and warning time.
- Adversarial auto-encoders,
- Fault early warning,
- Fault symptoms,
- Reciprocating compressor,
- Self-learning

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References(33)

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