A feature based frequency domain analysis algorithm for fault detection of induction motors

• Main Authors: WANG, Zhaoxia, CHANG, C. S., ZHANG Yifan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2011
• Subjects: Fast Fourier Transform; Fault detection; Independent Component Analysis; Induction motors fed from inverter; Operations Research, Systems Engineering and Industrial Engineering; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/5558; https://ink.library.smu.edu.sg/context/sis_research/article/6561/viewcontent/2011_A_Feature_Based_Frequency_Domain_Analysis_Algorithm_for_Fault_Detection_of_Induction_Motors_av.pdf
• Institution: Singapore Management University

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 stator current waveforms are enough to provide consistent diagnosis of inverter-fed induction motors at different frequencies. The proposed method also outperforms our previous time domain analysis method.