NDLI: Classifier Design for Sensor-Fault Tolerant Condition Monitoring in an Industrial Machine

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Raman, Srinivas Clarence, W. De Silva
Copyright Year	2009
Abstract	This paper presents a comparative evaluation of two classification schemes that can be used to accurately diagnose the health of machines in the presence of sensor failure. In the developed approach, multiple sensors acquire vibration and sound signals from a machine and the signals are represented using the Wavelet Packet Transform (WPT). A “wrapper” feature selection procedure is used to reduce the size of the feature set without sacrificing the classification accuracy. The performance of a Radial Basis Function Network (RBFN) is compared with that of a Support Vector Machine (SVM) by simulating and monitoring machine and sensor faults in an industrial fish cutting machine. Initial results show an 85% reduction in feature set size for an RBFN and a 92.5% reduction in feature set size for a SVM.
Sponsorship	Dynamic Systems and Control Division
Starting Page	637
Ending Page	643
Page Count	7
File Format	PDF
ISBN	9780791848920
DOI	10.1115/DSCC2009-2667
e-ISBN	9780791838600
Volume Number	ASME 2009 Dynamic Systems and Control Conference, Volume 1
Conference Proceedings	ASME 2009 Dynamic Systems and Control Conference
Language	English
Publisher Date	2009-10-12
Publisher Place	Hollywood, California, USA
Access Restriction	Subscribed
Subject Keyword	Condition monitoring Design Support vector machines Cutting Wavelets Feature selection Vibration Radial basis function networks Sensors Signals Machinery Failure
Content Type	Text
Resource Type	Article

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