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A new method for identifying induction motor bearing fault is introduced in this paper, it's based on the Volterra series which can describe the nonlinear transfer characteristics of system. Firstly, analyze the theory that bearing fault can cause torque vibration, and the simplify equation of stator current and voltage on bearing fault state is derived. The stator voltage and current signals are used as the input and output of Volterra series, then adaptive chaotic quantum particle swarm optimization (ACQPSO) is introduced for the identification of Volterra series time-domain kernel, and the bearing fault can be identified by the changes of nonlinear transfer characteristics. In order to validate the method, the induction motor bearing fault simulated test system is established in the lab to simulate the single point damage of bearing outer race which gradually expand; through the extraction of the changes of the kernel, the bearing fault and its severity can be identified. Thus verified the feasibility and effectiveness of the proposed method, the method is suitable for the prediction of the trends of bearing fault.

Abstract--The current feature for motor carried with natural mixed eccentricity and bearing fault is merely focused even though it is very important for motor online diagnosis. Considering both of the air-gap length change and torque vibration caused by bearing fault, the stator current expression were deduced for mixed eccentric motor. The new current characteristic frequencies for motor with bearing fault and mixed eccentricity were obtained. Finally, a series of simulation studies were conducted, the results show that the new eigenfrequencies are real and useful for online diagnosis of those motor in service.

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