Fault detection algorithms for real-time diagnosis in large-scale systems

Thiagalingam Kirubarajan; Venkatesh Narayana Malepati; Somnath Deb; Jie Ying

doi:10.1117/12.434244

20 July 2001 Fault detection algorithms for real-time diagnosis in large-scale systems

Thiagalingam Kirubarajan, Venkatesh Narayana Malepati, Somnath Deb, Jie Ying

Proceedings Volume 4389, Component and Systems Diagnostics, Prognosis, and Health Management; (2001) https://doi.org/10.1117/12.434244
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

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.

Citation Download Citation

Thiagalingam Kirubarajan, Venkatesh Narayana Malepati, Somnath Deb, and Jie Ying "Fault detection algorithms for real-time diagnosis in large-scale systems", Proc. SPIE 4389, Component and Systems Diagnostics, Prognosis, and Health Management, (20 July 2001); https://doi.org/10.1117/12.434244

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