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Physical Sciences and Mathematics Commons

Open Access. Powered by Scholars. Published by Universities.®

Singapore Management University

2011

Fault detection

Articles 1 - 2 of 2

Full-Text Articles in Physical Sciences and Mathematics

Online Fault Detection Of Induction Motors Using Frequency Domain Independent Components Analysis, Zhaoxia Wang, C. S. Chang Jun 2011

Online Fault Detection Of Induction Motors Using Frequency Domain Independent Components Analysis, Zhaoxia Wang, C. S. Chang

Research Collection School Of Computing and Information Systems

This paper proposes an online fault detection method for induction motors using frequency-domain independent component analysis. Frequency-domain results, which are obtained by applying Fast Fourier Transform (FFT) to measured stator current time-domain waveforms, are analyzed with the aim of extracting frequency signatures of healthy and faulty motors with broken rotor-bar or bearing problem. Independent components analysis (ICA) is applied for such an aim to the FFT results. The obtained independent components as well as the FFT results are then used to obtain the combined fault signatures. The proposed method overcomes problems occurring in many existing FFT-based methods. Results using laboratory-collected …


A Feature Based Frequency Domain Analysis Algorithm For Fault Detection Of Induction Motors, Zhaoxia Wang, C. S. Chang, Zhang Yifan Jun 2011

A Feature Based Frequency Domain Analysis Algorithm For Fault Detection Of Induction Motors, Zhaoxia Wang, C. S. Chang, Zhang Yifan

Research Collection School Of Computing and Information Systems

This paper studies the stator currents collected from several inverter-fed laboratory induction motors and proposes a new feature based frequency domain analysis method for performing the detection of induction motor faults, such as the broken rotor-bar or bearing fault. The mathematical formulation is presented to calculate the features, which are called FFT-ICA features in this paper. The obtained FFT-ICA features are normalized by using healthy motor as benchmarks to establish a feature database for fault detection. Compare with conventional frequency-domain analysis method, no prior knowledge of the motor parameters or other measurements are required for calculating features. Only one phase …