Online fault detection of induction motors using independent component analysis and fuzzy neural network

• Main Authors: WANG, Zhaoxia, CHANG, C. S., GERMAN, X., TAN, W.W.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2009
• Subjects: Online Fault Detection; Induction Motors; Independent Component Analysis; Fuzzy neural network; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/6867; https://ink.library.smu.edu.sg/context/sis_research/article/7870/viewcontent/2009__Online_Fault_Detection_of_Induction_Motors_Using_Independent_Component_Analysis_and_Fuzzy_Neural_Network.pdf
• Institution: Singapore Management University

This paper proposes the use of independent component analysis and fuzzy neural network for online fault detection of induction motors. The most dominating components of the stator currents measured from laboratory motors are directly identified by an improved method of independent component analysis, which are then used to obtain signatures of the stator current with different faults. The signatures are used to train a fuzzy neural network for detecting induction-motor problems such as broken rotor bars and bearing fault. Using signals collected from laboratory motors, the robustness of the proposed method for online fault detection is demonstrated for various motor load conditions.