Feature knowledge based fault detection of induction motors through the analysis of stator current data

• Main Authors: YANG, Ting, PEN, Haibo, WANG, Zhaoxia, CHANG, Che Sau
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Data analysis; fast Fourier transform (FFT); fault detection; feature knowledge database; independent component analysis (ICA); induction motors; stator current analysis; Numerical Analysis and Scientific Computing; Operations Research, Systems Engineering and Industrial Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/5546; https://ink.library.smu.edu.sg/context/sis_research/article/6549/viewcontent/2016_IEEE_TIM_FeatureKnowledgeBasedFaultDetectionofInductionMotors_av.pdf
• Institution: Singapore Management University

The fault detection of electrical or mechanical anomalies in induction motors has been a challenging problem for researchers over decades to ensure the safety and economic operations of industrial processes. To address this issue, this paper studies the stator current data obtained from inverter-fed...