Multiple Fault Identification Using Vibration Signal Analysis and Artificial Intelligence Methods
Authors:
- 1 September 2013
- journal article
- Published by Trans Tech Publications, Ltd. in Applied Mechanics and Materials
- Vol. 430, 63-69
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.Keywords
This publication has 11 references indexed in Scilit:
- A new feature for monitoring the condition of gearboxes in non-stationary operating conditionsMechanical Systems and Signal Processing, 2009
- Vibration condition monitoring of planetary gearbox under varying external loadMechanical Systems and Signal Processing, 2009
- Intelligent condition monitoring of a gearbox using artificial neural networkMechanical Systems and Signal Processing, 2007
- A Fault Detection and Identification System for Gearboxes using Neural Networks2006
- Feature Selection and Classification of Gear Faults Using SOMLecture Notes in Computer Science, 2005
- NOVELTY DETECTION IN A CHANGING ENVIRONMENT: REGRESSION AND INTERPOLATION APPROACHESJournal of Sound and Vibration, 2002
- ON-LINE AND INDIRECT TOOL WEAR MONITORING IN TURNING WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW OF MORE THAN A DECADE OF RESEARCHMechanical Systems and Signal Processing, 2002
- Novelty detection under changing environmental conditions2001
- Classification of faults in gearboxes ? pre-processing algorithms and neural networksNeural Computing and Applications, 1997
- Self-Organizing Maps1995