Abstract:

The detection of rolling-element bearing fault can be accomplished by monitoring and interpreting a variety of signals, including the vibration, the acoustic noise, and the stator current. The existence of a bearing fault as well as its specific fault type can be readily determined by performing frequency spectral analysis on the monitored signals with various signal processing techniques. However, this traditional approach, despite being simple and intuitive, is not able to identify the severity of a bearing fault in a quantitatively manner. Moreover, it is oftentimes tedious and time-consuming to apply this approach to electric machines with different power ratings, as the bearing fault threshold values need to be manually calibrated for each motor running at every possible speed and carrying any possible load. This paper thus proposes a quantitative approach to estimate the bearing fault severity based on the air gap displacement profile, which is reconstructed from the mutual inductance variation profile estimated from a quantitative electrical model that takes the stator current as input. In addition, the accuracy of the developed electrical model and the estimated bearing fault severity are validated by the simulation and experimental results, and the explicit air gap variation profile is reconstructed with the superposition of multiple Fourier Series terms estimated from the stator current via the proposed scheme. The proposed method offers a quantitative and universal bearing fault indicator for induction machines with any power ratings and operating under any speed and load conditions.