To improve fault diagnosis accuracy, a data-driven fault diagnosis model based on the adjustment Mahalanobis-Taguchi system (AMTS) was proposed. This model can analyze and identify the characteristics of vibration signals by using degradation monitoring as the classifier to capture and recognize the faults of product more accurately. To achieve this goal, we firstly used the modified ensemble empirical mode decomposition (MEEMD) scalar index to capture the bearing condition; then, by using the key intrinsic mode function (IMF) extracted by AMTS as the input of classifier, the optimized properties of bearing is decomposed and extracted effectively. Next, in order to improve the accuracy of the fault diagnosis we tested different modes; employing the modified health index (MHI), which is designed to overcome the shortcomings of the proposed health index as a classifier in single fault mode, and the deep neural networks (DNN) as a classifier in multi-fault mode. To evaluate the effectiveness of our model, the Case Western Reserve University (CWRU) bearing data were used for verification. Results indicated a strong robustness with 99.16% and 1.09s, 99.86% and 6.61s fault diagnosis accuracy in different data modes respectively. Furthermore, we argue that this data-driven fault diagnosis obviously lowers the maintenance cost of complex systems by significantly reducing the inspection frequency and improves future safety and reliability.

Lithium battery is a complex nonlinear time-varying system with several inconsistencies. The fault diagnosis method has difficulty making an early diagnosis of battery faults without obvious abnormalities. In fact, voltage inconsistency is a representative fault response. Therefore, the monitoring of voltage inconsistency is highly important for the safe and reliable operation of lithium batteries in Evehicles. The entropy method does not rely on an accurate analysis model and expert experience. Moreover, it does not consider the complex fault mechanism and system structure. Hence, it has gradually attracted widespread attention. Given such attention, a hybrid fault diagnosis method combining multiscale permutation entropy (MPE) and coefficient of variation (CV) is presented in this paper, and the improved MPE fault diagnosis model based on 3-sigma is emphasized. First, MPE and the 3-sigma rule are used to calculate the threshold, and the voltage inconsistency of the battery is judged by the threshold. Then, the location of the faulty cells is located by the CV. The superiority of the proposed method is proven by experimental data from the Yunzhitong platform of CRRC Electric Vehicle Co., Ltd. and a comparison of frontier methods. The proposed approach is feasible and promising in real E-vehicle applications. INDEX TERMS Electric vehicles;Lithium battery; multi-scale permutation entropy (MPE); coefficient of variation (CV) rule; 3-sigma rule

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