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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Erdinc, O. Brideau, C. Willett, P. Kirubarajan, T. |
| Copyright Year | 1996 |
| Abstract | This correspondence presents an approach to the detection and isolation of component failures in large-scale systems. In the case of sensors that report at rates of 1 Hz or less, the algorithm can be considered real time. The input is a set of observed test results from multiple sensors, and the algorithm's main task is to deal with sensor errors. The sensors are assumed to be of threshold test (pass/fail) type, but to be vulnerable to noise, in that occasionally true failures are missed, and likewise, there can be false alarms. These errors are further assumed to be independent conditioned on the system's diagnostic state. Their probabilities, of missed detection and of false alarm, are not known a priori and must be estimated (ideally along with the accuracies of these estimates) online, within the inference engine. Further, recognizing a practical concern in most real systems, a sparsely instantiated observation vector must not be a problem. The key ingredients to our solution include the multiple-hypothesis tracking philosophy to complexity management, a Beta prior distribution on the sensor errors, and a quickest detection overlay to detect changes in these error rates when the prior is violated. We provide results illustrating performance in terms of both computational needs and error rate, and show its application both as a filter (i.e., used to ldquocleanrdquo sensor reports) and as a standalone state estimator. |
| Page Count | 9 |
| File Size | 836659 |
| Starting Page | 1157 |
| Ending Page | 1165 |
| File Format | |
| ISSN | 10834419 |
| Volume Number | 38 |
| Issue Number | 4 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-08-01 |
| Publisher Place | U.S.A. |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | System testing Engines Sensor systems Error analysis Condition monitoring Real time systems Large-scale systems Filters State estimation Fault diagnosis system testing fault diagnosis machine health testing multihypothesis tracker (MHT) |
| Content Type | Text |
| Resource Type | Article |
| Subject | Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software |
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