Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 39, Issue 10, Pages 9821-9836Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2012.02.171
Keywords
Multi-sensor data fusion; Failure risk; Product and process innovation; Fuzzy Kalman filter approach; Fault detection and isolation
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This paper reports on a new integrated vehicle health maintenance system (IVHMS) based on fault detection and feedback. A fuzzy multi-sensor data fusion Kalman model was used to help reduce IVHMS failure risk. The IVHMS was tested, and sensors with and without faults were identified. The results demonstrate that multi-sensor data fusion based on fault detection and fuzzy Kalman feedback is an effective method of reducing risk in an IVHMS. Use of the fuzzy Kalman filter approach reduced the time needed to perform complex matrix manipulations to control higher order systems in the IVHMS. Moreover, the approach was able to capture the nonlinearity of engine operations under the influence of various anomalies. (C) 2012 Elsevier Ltd. All rights reserved.
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