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

Abstract

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. Through the residual spectrum and threshold comparison, a binary decision is made to find fault signatures in the spectrum. The statistical Q-function is used to generate the fault frequency band to distinguish between the fault signature and the noise signature. The experiment in this study is performed on a wastewater pump in an existing industrial facility to verify the proposed FDI. Two faulty conditions with mathematically known and mathematically unknown faulty signatures are experimented with and diagnosed. The study results present that the residual spectrum demonstrated to be more sensitive to fault signatures compare to the current spectrum. The proposed FDI has successfully shown to identify the fault signatures even for the mathematically unknown faulty signatures.

Permanent Magnet Synchronous Motor Driving Mechanical Transmission Fault Detection and Identification: A Model-Based Diagnosis Approach

“…Thus, it implies a healthy condition. A more detailed comparison between MCSA and the model-based approach for electric motor fault diagnosis can be found in [21]. Three different fault conditions are experimented for gear-to-gear transmission, as shown in Figure 5, and their residual current spectrum result is presented in Figure 8.…”

“…Black-box model identification such as subspace identification is mostly applied [20]. Data-driven state-space model derivation has been applied for induction motors in misalignment [21] and sensor fault diagnosis [22]. It has also been applied to PMSM, but for the control interest [23].…”

The Use of Digital Twins in Finite Element for the Study of Induction Motors Faults

“…However, it commonly operates exposed to unfavorable environmental conditions, such as the presence of humidity and dust. Still other factors, such as power quality problems and mechanical overload, corroborate the faults appearance in these motors [ 6 ].…”