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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chin, Hsinyung Danai, Kourosh |
| Copyright Year | 1991 |
| Description | Author affiliation: Graduate Research Assistant, Department of Mechanical Engineering, University of Massachusetts, Amherst, MA 01003 (Chin, Hsinyung) || Assistant Professor, Department of Mechanical Engineering, University of Massachusetts, Amherst, MA 01003 (Danai, Kourosh) |
| Abstract | Efficient extraction of fault signatures from sensory data is a major concern in fault diagnosis. This paper introduces a self-tuning method of fault signature extraction that enhances fault detection, minimizes false alarms, improves diagnosability, and reduces fault signature variability. The proposed method uses a Flagging Unit to convert the processed measurements to binary vectors, and utilizes nonparametric pattern classification techniques to estimate the fault signatures. The performance of the Flagging Unit, which relies on its adaptation algorithms to optimize its performance based upon a sample batch of measurement-fault vectors, is demonstrated in simulation. |
| Starting Page | 487 |
| Ending Page | 492 |
| File Size | 467513 |
| Page Count | 6 |
| File Format | |
| ISBN | 0879425652 |
| ISSN | 00220434 15289028 |
| DOI | 10.1115/1.2896468 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1991-06-26 |
| Publisher Place | USA |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | American Automatic Control Council(AACC) |
| Subject Keyword | Fault diagnosis Pollution measurement Pattern classification Noise measurement Data mining Fault detection Performance evaluation Signal processing algorithms Signal processing Mechanical engineering |
| Content Type | Text |
| Resource Type | Article |
| Subject | Instrumentation Control and Systems Engineering Information Systems Mechanical Engineering Computer Science Applications |
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