Title :
The Application of Multi-sensors Fusion in Vehicle Transmission System Fault Diagnosis
Author :
Wu, Xiaobing ; Liu, Shuangzhe ; Sharma, Dharmendra
Author_Institution :
Beijing Inst. of Technol., Beijing
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.
Keywords :
fault diagnosis; mechanical engineering computing; neural nets; power transmission (mechanical); sensor fusion; vehicle dynamics; artificial neural networks; decision-level vibration data; multi-sensor fusion; vehicle transmission system fault diagnosis; Artificial neural networks; Data mining; Fault diagnosis; Fuses; Robustness; Sensor fusion; Spectral analysis; Testing; Vehicles; Vibration measurement; artificial neural network; fault diagnosis; hybrid structure; information fusion; vehicle transmission system;
Conference_Titel :
Natural Computation, 2007. ICNC 2007. Third International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2875-5
DOI :
10.1109/ICNC.2007.709
