A fault diagnosis approach for gears using multidimensional features and intelligent classifier

Li, Zhixiong, Yan, Xinping, Yuan, Chengqing, Zhao, Jiangbin, and Peng, Zhongxiao (2010) A fault diagnosis approach for gears using multidimensional features and intelligent classifier. Noise and Vibration Worldwide, 41 (10). pp. 76-86.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website: http://dx.doi.org/10.1260/0957-4565.41.1...
 
15
2


Abstract

Gear mechanisms are an important element in a variety of industrial applications and about 80% of the breakdowns of the transmission machinery are caused by the gear failure. Efficient incipient fault detection and accurate fault diagnosis are therefore critical to machinery normal operation. A new hybrid intelligent diagnosis method is proposed in this work to identify multiple categories of gear defection. In this method, wavelet packet transform (WPT), empirical mode decomposition (EMD) and Wigner-Ville distributions (WVD), combined with autoregressive (AR) model algorithm, were performed on gear vibration signals to extract useful fault characteristic information. Then, multidimensional feature sets including energy distribution, statistical features and AR parameters were obtained to represent gear operation conditions from different perspectives. The nonlinear dimensionality reduction algorithm, i.e. isometric mapping (Isomap), was employed in statistics to mine the intrinsic structure of the feature space in a low-dimensional space, and thus to speed up the training of the probabilistic neural network (PNN) classifier and enhance its diagnosis accuracy. Experiments with different gear faults were conducted, and the vibration signals were measured under different drive speeds and loads. The analysis results indicate that the proposed method is feasible and effective in the gear multi-fault diagnosis, and the isolation of different gear conditions, including normal, single crack, compound fault of wear and spalling, etc., has been accomplished. Since the recognition results are available directly from the output of PNN, the proposed diagnosis technique provides the possibility to fulfill the automatic recognition on gear multiple faults.

Item ID: 16926
Item Type: Article (Refereed Research - C1)
Related URLs:
ISSN: 0957-4565
Date Deposited: 17 May 2011 06:23
FoR Codes: 09 ENGINEERING > 0913 Mechanical Engineering > 091304 Dynamics, Vibration and Vibration Control @ 100%
SEO Codes: 86 MANUFACTURING > 8614 Machinery and Equipment > 861403 Industrial Machinery and Equipment @ 100%
Downloads: Total: 2
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page