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TECHNICAL PAPERS

Multivariable Trend Analysis for System Monitoring Through Self-Organizing Neural Networks

[+] Author and Article Information
Siyu Zhang, R. Ganesan

Department of Mechanical Engineering, Concordia University, Montreal, Quebec, Canada, H3G 1M8

J. Dyn. Sys., Meas., Control 119(2), 223-228 (Jun 01, 1997) (6 pages) doi:10.1115/1.2801237 History: Received December 05, 1994; Online December 03, 2007

Abstract

For precise and reliable fault detection it is essential to consider simultaneously the changes in several diagnostic indices that are extracted from the on-line vibration signal. Existing machine condition monitoring systems consider each diagnostic index separately. Development of an automated diagnostic procedure that considers simultaneously several diagnostic indices is the objective of the present paper. The multivariable trend analysis of on-line vibration signals is deployed as the basis for this procedure. An efficient self-organizing neural network algorithm that is highly suitable to this diagnostic procedure is developed and deployed. Applications to both a bearing system as well as a gearbox system are fully developed and demonstrated.

Copyright © 1997 by The American Society of Mechanical Engineers
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