Intelligent diagnosis of mechanical faults of in-wheel motor based on improved artificial hydrocarbon networks

For the driving safety of electric vehicle (EV), intelligent diagnosis based on artificial hydrocarbon networks (AHNs) is proposed to detect mechanical faults of in-wheel motor (IWM) which is a promising force pattern of EV. AHNs, a novel mathematical model of supervised learning algorithm, can encapsulate or inherit or mix any information, then are adapted to deal with serious external interference and the variable operating conditions. Based on the basic AHNs, complex error function is proposed to optimize more information of classification targets, and distance error ratio is defined to evaluate the performance. Then, the improved AHNs is employed to build two intelligent diagnosis systems namely one-stop diagnosis and sequential diagnosis, which select the same and different symptom parameters as the object of a follow-on process, respectively. The effectiveness of the proposed methods is validated by two case studies of Case Western Reserve University dataset and mechanical faults data from IWM's test bench.