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.
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June 1997
Technical Papers
Multivariable Trend Analysis for System Monitoring Through Self-Organizing Neural Networks
Siyu Zhang,
Siyu Zhang
Department of Mechanical Engineering, Concordia University, Montreal, Quebec, Canada, H3G 1M8
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R. Ganesan
R. Ganesan
Department of Mechanical Engineering, Concordia University, Montreal, Quebec, Canada, H3G 1M8
Search for other works by this author on:
Siyu Zhang
Department of Mechanical Engineering, Concordia University, Montreal, Quebec, Canada, H3G 1M8
R. Ganesan
Department of Mechanical Engineering, Concordia University, Montreal, Quebec, Canada, H3G 1M8
J. Dyn. Sys., Meas., Control. Jun 1997, 119(2): 223-228 (6 pages)
Published Online: June 1, 1997
Article history
Received:
December 5, 1994
Online:
December 3, 2007
Citation
Zhang, S., and Ganesan, R. (June 1, 1997). "Multivariable Trend Analysis for System Monitoring Through Self-Organizing Neural Networks." ASME. J. Dyn. Sys., Meas., Control. June 1997; 119(2): 223–228. https://doi.org/10.1115/1.2801237
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