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.
Highlights
The industrial rotating machinery (RM) utilizes an induction motor (IM) as the actuator, such as a pump, fan, conveyor, or compressor
It is common that fault detection is performed on the time-domain residual data [8,13,17]
Time-domain residual data evaluation for fault detection will determine the success of the fault detection if the fault alters the output of the system, as an example, the current signal in our study
Summary
The industrial RM utilizes an IM as the actuator, such as a pump, fan, conveyor, or compressor. IM-driven RM often run not in a proper condition in which their efficiency may reduce, and the energy cost may increase. If this condition stays for a more extended period, it will lead to machine failure and may stop the entire production process with the risks of safety, expensive production loss, a time-consuming, and costly repairing process. Popular and promising FDI strategies in recent years include motor current signature analysis (MCSA) and vibration analysis [1–3]. Both methods examine the abnormal harmonic modulation at the specific fault characteristic frequencies in the frequency spectrum by utilizing the Fast Fourier Transform (FFT) algorithm. The detection and identification results can be distorted, and it reduces the effectiveness of MCSA based FDI
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