Bearing Fault Detection Using Autoregressive Modeling

Typical components of rotating machinery include bearings with rolling parts. Vibration analysis is widely used to assess the overall health and condition of spinning machinery. To achieve exceptionally dependable operations, finding faults is crucial. The vibration signals can be used to ascertain the health and status of the bearing. Bearing faults in rotating machinery can lead to serious operational issues and result in costly downtime and maintenance. To avoid unexpected failures and maximize maintenance plans, early diagnosis and detection of bearing defects are essential. A novel approach for bearing fault detection using autoregressive (AR) modeling is presented in this paper. The AR model effectively identifies the bearing faults by capturing the dynamic behavior of bearing vibration data. Experimental results indicate that the proposed method of the autoregression technique exhibits excellent results in comparison to the Fast Fourier Transform technique in identifying various bearing faults.