Stator current operation compensation (SCOC): A novel preprocessing method for deep learning-based fault diagnosis of permanent magnet synchronous motors under variable operating conditions

Abstract

This paper proposes a novel preprocessing method, namely stator current operation compensation (SCOC), for deep learning-based fault diagnosis of a permanent magnet synchronous motor (PMSM) under variable operating conditions. To solve the problem that a change in operating conditions modifies characteristics of stator current signals in a way that makes it hard to discriminate fault modes, SCOC includes two stages: 1) tacho-less resampling and 2) main operating component subtraction with rescaling. First, the stator current signal is resampled to contain the same cycle for each deep learning input. Next, the signal is transformed to alleviate amplitude change by torque and to emphasize relatively small fault components. The effectiveness of the proposed method was validated by applying it to experimental data acquired under different types of PMSM operations. The results showed that each SCOC stage can reduce the variability between the data and effectively increase the fault diagnosis performance of PMSMs.