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.

Issue Section:
Technical Papers
Topics:
Artificial neural networks, Self-organization, System monitoring, Trend analysis, Signals, Vibration, Algorithms, Bearings, Condition monitoring, Flaw detection, Machinery, Mechanical drives
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