Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method

被引:132
作者
Li, Zhixiong [1 ,2 ]
Yan, Xinping [1 ,2 ]
Yuan, Chengqing [1 ,2 ]
Peng, Zhongxiao [3 ]
Li, Li [4 ]
机构
[1] Wuhan Univ Technol, Sch Energy & Power Engn, Reliabil Engn Inst, Wuhan 430063, Peoples R China
[2] Wuhan Univ Technol, Minist Transportat, Key Lab Marine Power Engn & Technol, Wuhan 430063, Peoples R China
[3] James Cook Univ, Sch Engn & Phys Sci, Townsville, Qld 4811, Australia
[4] China Three Gorges Univ, Hubei Key Lab Hydroelect Machinery Design & Maint, Yichang 443002, Peoples R China
关键词
Gear fault diagnosis; Virtual prototype; Wavelet AR model; PCA; EXTRACTION; SIGNALS;
D O I
10.1016/j.ymssp.2011.02.017
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Gear systems are an essential element widely used in a variety of industrial applications. Since approximately 80% of the breakdowns in transmission machinery are caused by gear failure, the efficiency of early fault detection and accurate fault diagnosis are therefore critical to normal machinery operations. Reviewed literature indicates that only limited research has considered the gear multi-fault diagnosis, especially for single, coupled distributed and localized faults. Through virtual prototype simulation analysis and experimental study, a novel method for gear multi-fault diagnosis has been presented in this paper. This new method was developed based on the integration of Wavelet transform (WO technique, Autoregressive (AR) model and Principal Component Analysis (PCA) for fault detection. The WT method was used in the study as the de-noising technique for processing raw vibration signals. Compared with the noise removing method based on the time synchronous average (TSA), the WT technique can be performed directly on the raw vibration signals without the need to calculate any ensemble average of the tested gear vibration signals. More importantly, the WT can deal with coupled faults of a gear pair in one operation while the TSA must be carried out several times for multiple fault detection. The analysis results of the virtual prototype simulation prove that the proposed method is a more time efficient and effective way to detect coupled fault than TSA, and the fault classification rate is superior to the TSA based approaches. In the experimental tests, the proposed method was compared with the Mahalanobis distance approach. However, the latter turns out to be inefficient for the gear multi-fault diagnosis. Its defect detection rate is below 60%, which is much less than that of the proposed method. Furthermore, the ability of the AR model to cope with localized as well as distributed gear faults is verified by both the virtual prototype simulation and experimental studies. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:2589 / 2607
页数:19
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