New Fuzzy Observer Fault Pattern Detection by NARX-Laguerre Model Applied to the Rotating Machine

Abstract

A rotating machine is a ubiquitous product in industries. Inner, outer, and roller cracking in bearings can cause structural failure of the rotating machines and possibly decrease the service life of different industrial systems such as motors. The nonlinearity and complexity associated with the uncertain and unknown behavior inherent in rotating machines lead to difficulty in the detection and identification of bearings’ cracks in real-time. To address these issues, the combination of indirect Proportional-Integral-Derivative (PID) observer and fuzzy logic approach is proposed in the paper to detect the fault pattern in the bearing. To do this, firstly the indirect fuzzy observer is modeled by the nonlinear autoregressive with Laguerre filter that is improved by the fuzzy technique. After signal estimation using indirect fuzzy observer, the difference (residual signals) between original and estimated signals are computed. Finally, the machine learning technique is utilized for residual signal pattern detection. The Case Western Reverse University Bearing Dataset (CWRUBD) is used to test the proposed scheme.