Autoregressive based diagnostics scheme for detection of bearing faults

An investigation into the vibration characteristics of a ‘Roots and Claws’ based dry vacuum pump under different operating conditions was conducted. An AutoRegressive (AR)-based condition monitoring algorithm was developed and tested on both a fault-free and a pump with an implanted ceramic bearing with an inner race defect at the High Vacuum (HV) end. The investigation provided some in-depth understanding of the effects of different operating conditions such as speed and load on the vibration of the pump. The first key step in the fault detection scheme was accurate determination of the running speed of the pump. It was observed that the rotating speed of the pump’s rotor shaft on which the bearing case was directly connected to was often less than the set speed of the pump due to rotor slip. The second step was envelope demodulation of the time domain vibration signals where the resonance excited by the fault-induced impacts was identified and the vibration signal were bandpass filtered around the resonant peak. The third step is spectral estimation using parametric-based method of AR modelling. The advantage of the AR method is that it can work with smaller sample sizes and sampling rates compared to the more traditional approach of FFT (Fast Fourier Transform) and achieve far superior resolution capabilities. The analysis results showed that the effect of actual speed was predominant in the detection of bearing faults as this was the speed that was used in the calculations of the bearing defect frequencies and had to be determined very accurately. Initial results show that the fault diagnostic scheme is very promising and the bearing fault could be accurately determined at all speeds.