Stress Wave Analysis Based Prognostic Health Management

WU Tianshu; CHEN Shuyu; SAL Tareen; WU Peng

doi:10.1049/cje.2018.02.013

Volume 27 Issue 3

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WU Tianshu, CHEN Shuyu, SAL Tareen, et al., “Stress Wave Analysis Based Prognostic Health Management,” Chinese Journal of Electronics, vol. 27, no. 3, pp. 565-572, 2018, doi: 10.1049/cje.2018.02.013

Citation:

WU Tianshu, CHEN Shuyu, SAL Tareen, et al., “Stress Wave Analysis Based Prognostic Health Management,” Chinese Journal of Electronics, vol. 27, no. 3, pp. 565-572, 2018, doi: 10.1049/cje.2018.02.013

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Stress Wave Analysis Based Prognostic Health Management

doi: 10.1049/cje.2018.02.013

1.
College of Computer Science, Chongqing University, Chongqing 400044, China;
2.
Nuclear Division, Curtiss Wright Corporation, Idaho Falls ID 83402, USA;
3.
Chongqing Chuanyi Automation Co., Ltd., Chongqing 401121, China

Funds: This work is supported by the State Ministry of Industry and Information Technology (2015 Intelligent manufacturing special No.82).

More Information

Corresponding author: WU Peng (corresponding author) was born in 1963. He received the Ph.D. degree in control science from Chongqing University. He is a professorship engineer and Ph.D. supervisor in Chongqing Chuanyi Automation Co., Ltd. His research interests include sensor, intelligent instrument and distributed control system. (Email:wupeng@cqcy.com)
Received Date: 2017-09-19
Rev Recd Date: 2017-11-09
Publish Date: 2018-05-10

Abstract

Abstract

The stress wave sensor detect and process the electronic signal of friction, mechanical shock and dynamic load on equipment moving parts, the stress wave analysis are fulfilled by using the time domain and frequency domain feature extraction software, Polynomial neural network (PNN) and data fusion technology. The equipment status are quantitatively analyzed, the equipment fault are accurately predicted. Compared with the current adopted other analysis technologies, the system can monitor the operation condition of the equipment better in real-time, predict the fault earlier. The production safety is guaranteed, the equipment maintenance cost is reduced, and the production efficiency is improved.
- Stress wave analysis,
- Feature extraction software,
- Polynomial neural network (PNN),
- Data fusion

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