Online bearing fault detection using linear prediction and nonlinear energy operator

Online condition monitoring is essential for the reliability of rolling element bearing in mechatronic systems. This allows recording continuous information on the bearing conditions and taking the appropriate actions. Detecting faults in rolling element bearing is a real challenge in Fault Detection (FD) cognizance. In addition, fault diagnosis is essential to reaches the root cause of failure. In this paper an online condition monitoring system for bearing fault detection is presented. Linear Prediction Coefficient (LPC) is first applied to the signal for noise elimination. The frequency domain analysis is be implemented. Nonlinear energy operator (NEO) is used to amplify the signal that contain high energy and minimize the low energy signal. The proposed method is implemented using Labview environment which enables online remote control of data acquisition as well as real-time analysis.