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.
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ASME 2009 Dynamic Systems and Control Conference
October 12–14, 2009
Hollywood, California, USA
Conference Sponsors:
- Dynamic Systems and Control Division
ISBN:
978-0-7918-4892-0
PROCEEDINGS PAPER
Classifier Design for Sensor-Fault Tolerant Condition Monitoring in an Industrial Machine
Srinivas Raman,
Srinivas Raman
University of British Columbia, Vancouver, BC, Canada
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Clarence W. de Silva
Clarence W. de Silva
University of British Columbia, Vancouver, BC, Canada
Search for other works by this author on:
Srinivas Raman
University of British Columbia, Vancouver, BC, Canada
Clarence W. de Silva
University of British Columbia, Vancouver, BC, Canada
Paper No:
DSCC2009-2667, pp. 637-643; 7 pages
Published Online:
September 16, 2010
Citation
Raman, S, & de Silva, CW. "Classifier Design for Sensor-Fault Tolerant Condition Monitoring in an Industrial Machine." Proceedings of the ASME 2009 Dynamic Systems and Control Conference. ASME 2009 Dynamic Systems and Control Conference, Volume 1. Hollywood, California, USA. October 12–14, 2009. pp. 637-643. ASME. https://doi.org/10.1115/DSCC2009-2667
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