Share Email Print

Proceedings Paper

Fault detection algorithms for real-time diagnosis in large-scale systems
Author(s): Thiagalingam Kirubarajan; Venkatesh Narayana Malepati; Somnath Deb; Jie Ying
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

In this paper, we present a review of different real-time capable algorithms to detect and isolate component failures in large-scale systems in the presence of inaccurate test results. A sequence of imperfect test results (as a row vector of 1's and 0's) are available to the algorithms. In this case, the problem is to recover the uncorrupted test result vector and match it to one of the rows in the test dictionary, which in turn will isolate the faults. In order to recover the uncorrupted test result vector, one needs the accuracy of each test. That is, its detection and false alarm probabilities are required. In this problem, their true values are not known and, therefore, have to be estimated online. Other major aspects in this problem are the large-scale nature and the real-time capability requirement. Test dictionaries of sizes up to 1000 x 1000 are to be handled. That is, results from 1000 tests measuring the state of 1000 components are available. However, at any time, only 10-20% of the test results are available. Then, the objective becomes the real-time fault diagnosis using incomplete and inaccurate test results with online estimation of test accuracies. It should also be noted that the test accuracies can vary with time --- one needs a mechanism to update them after processing each test result vector. Using Qualtech's TEAMS-RT (system simulation and real-time diagnosis tool), we test the performances of 1) TEAMS-RT's built-in diagnosis algorithm, 2) Hamming distance based diagnosis, 3) Maximum Likelihood based diagnosis, and 4) Hidden Markov Model based diagnosis.

Paper Details

Date Published: 20 July 2001
PDF: 12 pages
Proc. SPIE 4389, Component and Systems Diagnostics, Prognosis, and Health Management, (20 July 2001); doi: 10.1117/12.434244
Show Author Affiliations
Thiagalingam Kirubarajan, Univ. of Connecticut (Canada)
Venkatesh Narayana Malepati, Qualtech Systems, Inc. (United States)
Somnath Deb, Qualtech Systems, Inc. (United States)
Jie Ying, Univ. of Connecticut (China)

Published in SPIE Proceedings Vol. 4389:
Component and Systems Diagnostics, Prognosis, and Health Management
Peter K. Willett; Thiagalingam Kirubarajan, Editor(s)

© SPIE. Terms of Use
Back to Top