Multiple Fault Identification Using Vibration Signal Analysis and Artificial Intelligence Methods

Abstract:

Paper addresses the implementation of feature based artificial neural networks and self-organized feature maps with the vibration analysis for the purpose of automated faults identification in rotating machinery. Unlike most of the research in this field, where a single type of fault has been treated, the research conducted in this paper deals with rotating machines with multiple faults. Combination of different roller elements bearing faults and different gearbox faults is analyzed. Experimental work has been conducted on a specially designed test rig. Frequency and time domain vibration features are used as inputs to fault classifiers. A complete set of proposed vibration features are used as inputs for self-organized feature maps and based on the results they are used as inputs for supervised artificial neural networks. The achieved results show that proposed set of vibration features enables reliable identification of developing bearing and gear faults in geared power transmission systems.

• Periodical: Applied Mechanics and Materials (Volume 430)
• Pages: 63-69
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.430.63
• Online since: September 2013
• Authors: Ninoslav Zuber, Dragan Cvetkovic, Rusmir Bajrić
• Keywords: Artificial Neural Network (ANN), Bearing Fault, Gearbox Fault, Self-Organized Feature Map, Vibration Analysis

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