The Application of Multi-sensors Fusion in Vehicle Transmission System Fault Diagnosis

Authors:
Xiaobing Wu;Shuangzhe Liu;Dharmendra Sharma
Affiliations:
Beijing Institute of Technology, China;University of Canberra, Australia;University of Canberra, Australia
Venue:
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
Year:
2007

Citing 0
Cited 1

Toward reducing failure risk in an integrated vehicle health maintenance system: A fuzzy multi-sensor data fusion Kalman filter approach for IVHMS

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Multi-sensors fusion technology is adopted for fault diagnosis of vehicle transmission system. By using hybrid pattern fusion based on artificial neural networks (ANN), the robustness of the diagnosing system is improved greatly. This hybrid fusion pattern avoids working with a great deal of original data from sensors, while it has the advantage of less information lost. At the same time, the diagnosis effect is improved by using feature-level and decision-level vibration data and original-level lube data.