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RESEARCH PAPERS

Multiple Fault Detection and Isolation Using the Haar Transform, Part 1: Theory

[+] Author and Article Information
C. K. H. Koh

School of Mechanical and Production Engineering, Nanyang Technological University, Singapore

J. Shi

Industrial and Operations Engineering Department, University of Michigan, Ann Arbor, MI 48109

W. J. Williams

Electrical Engineering and Computer Science Department, University of Michigan, Ann Arbor, MI 48109

J. Ni

Mechanical Engineering and Applied Mechanics Department, University of Michigan, Ann Arbor, MI 48109

J. Manuf. Sci. Eng 121(2), 290-294 (May 01, 1999) (5 pages) doi:10.1115/1.2831218 History: Received October 01, 1996; Revised April 01, 1998; Online January 17, 2008

Abstract

Most manufacturing processes involve several process variables which interact with one another to produce a resultant action on the part. A fault is said to occur when any of these process variables deviate beyond their specified limits. An alarm is triggered when this happens. Low cost and less sophisticated detection schemes based on threshold bounds on the original measurements (without feature extraction) often suffer from high false alarm and missed detection rates when the process measurements are not properly conditioned. They are unable to detect frequency or phase shifted fault signals whose amplitudes remain within specifications. They also provide little or no information about the multiplicity (number of faults in the same process cycle) or location (the portion of the cycle where the fault was detected) of the fault condition. A method of overcoming these limitations is proposed in this paper. The Haar transform is used to generate sets of detection signals from the original measurements of process monitoring signals. By partitioning these signals into disjoint segments, mutually exclusive sets of Haar coefficients can be used to locate faults at different phases of the process. The lack of a priori information on fault condition is overcomed by using the Neyman-Pearson criteria for the uniformly most powerful form (UMP) of the likelihood ratio test (LRT).

Copyright © 1999 by The American Society of Mechanical Engineers
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