Nonlinear Autoregressive with Exogenous Model to Diagnosis Aircraft Motor Faults Under Different Operating Conditions

• DOI: 10.1007/s42835-020-00595-3
• Publication Date: 2020-11-09T09:08:56Z
• Authors: Wathiq R. Abed; Muhanad A. Ahmed
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

Robust fault analysis (FA) including the diagnosis of faults and predicting their level of fault severity is necessary to optimize maintenance and improve reliability. This study aimed at presenting a technique to diagnosis faults of electronic switch in permanent magnet synchronous motor in Aircraft. The current output of both thyristor bridges and the diode of system excitation is monitored under healthy and faulty operations. Features extracted at different operations using Multi-scale wavelet decomposition (MSWD) to extract the useful features. MSWD features are used to train nonlinear autoregressive with exogenous model which sequentially operated to evaluate the fault level in case open circuit that developing across a switch under different operating condtions. The two models have been tested and designed due to the simulated data, where the results showed acceptable effectiveness in the diagnosis of various types of fault.