James Rodger, MIS and Decision Sciences, published an article titled “Toward Reducing Failure Risk in an Integrated Vehicle Health Maintenance System: A Fuzzy Multi-Sensor Data Fusion Kalman Filter Approach for IVHMS,” in Expert Systems with Applications, a refereed journal with an impact factor of 2.2.
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.