@inproceedings{eab7b85c8b3d436ea2343196d0810bc1,
title = "Bearing Fault Diagnosis Based on Adaptive Multiclass-Mahalanobis-Taguchi System",
abstract = "To diagnose faults of bearings accurately, a fault diagnosis method based on Empirical Mode Decomposition (EMD), Singular Value Decomposition (SVD) and adaptive Multiclass-Mahalanobis-Taguchi System (aMMTS) is proposed in this paper. The condition of the bearing is monitored in real time by sensors. Then, the vibration signal is decomposed by EMD and the features are extracted by using SVD. Then, a novel adaptive Multiclass-Mahalanobis-Taguchi system is proposed for fault diagnosis. The hybrid method based on EMD-SVD and adaptive Multiclass-Mahalanobis-Taguchi system overcomes the shortcomings in the Mahalanobis-Taguchi system in terms of over-fitting and non-adaptive feature selection for fault diagnosis and has some advantages over the traditional auxiliary noise fault analysis method when dealing with nonlinear signal, and can diagnose the bearing fault without manual intervention. The effectivity and feasibility of the proposed method is validated by an experiment.",
keywords = "Adaptive Multiclass-Mahalanobis-Taguchi System, Bearing, EMD, Fault diagnosis, SVD",
author = "Ning Wang and Limin Jia and Zhipeng Wang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018 ; Conference date: 26-10-2018 Through 28-10-2018",
year = "2019",
month = jan,
day = "4",
doi = "10.1109/PHM-Chongqing.2018.00197",
language = "英语",
series = "Proceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1120--1125",
editor = "Ping Ding and Chuan Li and Shuai Yang and Ping Ding and Rene-Vinicio Sanchez",
booktitle = "Proceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018",
address = "美国",
}