Title: Application of artificial neural networks for the fault detection and diagnosis of active magnetic bearings
Authors: Anand S. Reddy; Praveen Kumar Agarwal; Satish Chand
Addresses: Department of Mechanical Engineering, KLE Institute of Technology, Hubballi – 580030, Karnataka State, India ' Department of Mechanical Engineering, Motilal Nehru National Institute of Technology Allahabad, Allahabad – 211004, Uttar Pradesh, India ' Department of Mechanical Engineering, G.L. Bajaj Institute of Technology & Management, Knowledge Park III, Greater Noida-201306, Uttar Pradesh, India
Abstract: Active magnetic bearings (AMBs) are the class of advanced mechatronic systems. The stable operation of these depends on the normal operation of its sensors and actuators whose malfunctioning may disturb the stability of the supported rotor. Therefore, online fault detection and diagnosis (FDD) of sensors and actuators of AMB system is essential for safe and reliable operation. Model based FDD requires complex mathematical modelling and has higher chances of subjected to modelling errors. Redundant sensors and actuators based FDD incurs additional cost and also requires additional space for installation. Therefore, in the present work, simulation data driven artificial neural network (ANN) based methodology with statistical analysis is proposed for FDD of AMBs. Faults in single position-sensor or actuator as well as in multiple sensors and actuators are detected and diagnosed simultaneously. Various types of faults such as bias, multiplicative and noise addition are considered for the diagnosis.
Keywords: active magnetic bearing; sensors and actuators; fault detection and diagnosis; artificial neural network.
International Journal of Mechatronics and Automation, 2018 Vol.6 No.2/3, pp.130 - 142
Received: 20 Apr 2018
Accepted: 14 May 2018
Published online: 26 Aug 2018 *