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Vehicle failure prediction technology is an important part of PHM (Prognostic and Health Management) technology, which is of great significance to the safety of vehicles and to improve driving safety. Based on the vehicle operating data collected by the on-board terminal (T-box) of the telematics system, the research on the state of vehicle failure is conducted. First, this paper conducts statistical analysis on vehicle historical fault data. Preprocessing procedures such as cleaning, integration, and protocol are performed to group the data set. Then, three indexes including recency (R) frequency (F), and days (D) are selected to construct a vehicle security status subdivision system, and K -Means algorithm is utilized to divide different vehicle categories from the perspective of vehicle value. Labeled information of vehicles in different security status are further established.

This paper presents a novel torque distribution strategy (TDS) and a modified regenerative braking strategy (MRBS) for distributed-drive electric vehicle (DDEV) considering energy saving and brake stability. The presented TDS minimizes the energy consumption from battery in driving process. In order to overcome the shortcomings by using polynomial approximation for motor efficiency and the local minima problem, an exhaustive search method (ESM) is proposed to obtain the optimal front-rear torque distribution ratio. First, the power summation of four in-wheel motors is selected as the cost function of the optimization problem. Second, the ESM is designed to obtain the optimal torque distribution ratio according to current torque demand and motor speed based on motor efficiency map. Maximum motor torque and tire-road conditions are taken as constraints. Third, a MRBS is proposed to improve energy recovery performance by take ECE R13 and motor efficiency into account.

Gearbox fault diagnosis is one of the core research areas in the field of rotating machinery condition monitoring. The signal processing-based bearing fault diagnosis in the gearbox is considered as challenging as the vibration signals collected from acceleration transducers are, in general, a mixture of signals originating from an unknown number of sources, i.e. an underdetermined blind source separation (UBSS) problem. In this study, an effective UBSS-based algorithm solution, that combines empirical mode decomposition (EMD) and kernel independent component analysis (KICA) method, is proposed to address the technical challenge. Firstly, the nonlinear mixture signals are decomposed into a set of intrinsic mode function components (IMFs) by the EMD method, which can be combined with the original observed signals to reconstruct new observed signals. Thus, the original problem can be effectively transformed into over-determined BSS problem.