The characteristic frequencies of different types of bearing faults can be calculated by a well-defined frequencybased model that depends on the motor speed, the bearing geometry and the specific location of a defect inside a bearing. Therefore, the existence of a bearing fault as well as its specific fault type can be readily determined by performing frequency spectral analyses on the monitored signals. 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 often 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 a bearing fault severity based on the air gap displacement profile, which is reconstructed from the mutual inductance variation profile estimated from a novel electrical model that only takes the stator current as input. In addition, the accuracy of the electrical model and the estimated bearing fault severity are validated by simulation results. The proposed method can be used to monitor bearing faults in induction machines with any power ratings that operate under any speeds and loads, and a bearing fault alarm will be triggered if the fault severity exceeds a universal threshold value.