NDLI: Machine Fault Detection Using Genetic Programming

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Samanta, B.
Copyright Year	2005
Abstract	Applications of genetic programming (GP) include many areas. However applications of GP in the area of machine condition monitoring and diagnostics is very recent and yet to be fully exploited. In this paper, a study is presented to show the performance of machine fault detection using GP. The time domain vibration signals of a rotating machine with normal and defective gears are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to GP for two class (normal or fault) recognition. The number of features and the features are automatically selected in GP maximizing the classification success. The results of fault detection are compared with genetic algorithm (GA) based artificial neural network (ANN)- termed here as GA-ANN. The number of hidden nodes in the ANN and the selection of input features are optimized using GAs. Two different normalization schemes for the features have been used. For each trial, the GP and GA-ANN are trained with a subset of the experimental data for known machine conditions. The trained GP and GA-ANN are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The results compare the effectiveness of both types of classifiers with GP and GA based selection of features.
Sponsorship	Design Engineering Division and Computers and Information in Engineering Division
Starting Page	591
Ending Page	599
Page Count	9
File Format	PDF
ISBN	0791847381
DOI	10.1115/DETC2005-84642
e-ISBN	0791837661
Volume Number	Volume 1: 20th Biennial Conference on Mechanical Vibration and Noise, Parts A, B, and C
Conference Proceedings	ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
Language	English
Publisher Date	2005-09-24
Publisher Place	Long Beach, California, USA
Access Restriction	Subscribed
Subject Keyword	Computer programming Flaw detection Feature selection Vibration Artificial neural networks Machinery Mechanical drives Genetic algorithms Condition monitoring Gases Gears Feature extraction Signals
Content Type	Text
Resource Type	Article

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