Abstract: Affinity propagation (AP) of uncertainty hidden Markov model (HMM) and statistical clustering method are the two commonly used methods for fault diagnosis of equipment. However, since the structure of a electric car charging pile is complex and there are few fault samples on it, the above two methods are not ideal for fault diagnosis. According to the characteristics of charging pile with fault diagnosis, taking into account the AP clustering fast and accurate fault feature extraction and HMM powerful capability of fault classification, a fault diagnosis method of charging pile is presented based on AP-HMM hybrid model in this paper. At the same time, in order to discuss the long-term nature of the charging pile, the Markov equilibrium equations are used to obtain the stable probability of fault. The experimental results verify the correctness of the above theoretical analysis, and the results show that the AP-HMM hybrid model has the advantage of high diagnostic accuracy compared with the traditional model. The hybrid model proposed in this paper has certain feasibility and universality, and can be applied to the fault diagnosis of other electronic equipments with low speed and high precision.

Key words: affinity propagation clustering, hidden Markov model, charging piles;stable distribution, fault diagnosis