@inproceedings{290c5c85f3274220842c3e42f5ae709b,
title = "Wavelet-based statistical health monitoring and fault diagnosis method",
abstract = "In this paper, a wavelet-based statistical method is proposed for health monitoring and fault diagnosis. This method integrates the statistical process control technology and the discrete wavelet transform. A statistical indicator based on discrete wavelet transform is constructed, and the X-bar chart is used to monitor the indicator. The fault frequency can be identified in the Hilbert envelope spectrum of the signal which is reconstructed by the out-of-control levels. Thus with the proposed method, one can not only detect a process change but also identify the fault type. An experimental study is conducted to demonstrate the effectiveness of the proposed method.",
keywords = "Bearing fault, Fault diagnosis, Monitoring, SPC, Wavelet transform",
author = "Wei Fan and Qiang Zhou",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017 ; Conference date: 16-08-2017 Through 18-08-2017",
year = "2017",
month = dec,
day = "9",
doi = "10.1109/SDPC.2017.70",
language = "English (US)",
series = "Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "334--338",
editor = "Wei Guo and {de Oliveira}, {Jose Valente} and Chuan Li and Yun Bai and Ping Ding and Juanjuan Shi",
booktitle = "Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017",
}