A Novel Fault Detection and Identification Framework for Rotating Machinery Using Residual Current Spectrum.

A novel framework of model-based fault detection and identification (MFDI) for induction motor (IM)-driven rotating machinery (RM) is proposed in this study. A data-driven subspace identification (SID) algorithm is employed to obtain the IM state-space model from the voltage and current signals in a quasi-steady-state condition. This study aims to improve the frequency-domain fault detection and identification (FDI) by replacing the current signal with a residual signal where a thresholding method is applied to the residual signal.