Detection of the stator inter-turn fault using the energy feature of the wavelet coefficients obtained through continuous wavelet transform

This research aims to investigate the application of a fault detection method to the stator part of the Brush-Less DC (BLDC) motor. Indeed, it is important to ensure that the motor is operating in a healthy mode and in any other case, it is of great importance to detect the fault as soon as possible to prevent further ruin of the major system. To this end, a sub-branch method of wavelet transform analysis, called Continuous Wavelet Transform (CWT), was utilized to observe the short-circuit fault in the stator coils. In addition, a novel simulator of the BLDC motor was developed by forming an interconnection between ADAMS and MATLAB, which incorporated different electrical and mechanical components. As a result, a close-to-reality model of the BLDC motor was achieved that ensured more accurate evaluation of the proposed method. An energy-type feature was then suggested to characterize fault occurrence. The amount of the normalized energy was obtained for one of the wavelet coefficient signals caused by the CWT and the energy was compared with a predefined threshold amount of energy for that signal to facilitate the detection of the flawed performance of the stator. The proposed method can be further validated through different simulations. (c) 2023 Sharif University of Technology. All rights reserved.