Rolling bearing quality evaluation based on a morphological filter and a Kolmogorov complexity measure

Bearing defective inspection plays a vital role in bearing quality control. Unlike signals in the process of condition monitoring and fault diagnosis, the signal characteristic of defective bearings is much weaker and difficult to be quantified through the acceleration based techniques. In this paper, a novel system is developed to inspect automatically the small defects of roller bearings for on-line quality control. Rather than using acceleration based techniques the system employs a high sensitive eddy current sensor to measure the displacement profiles of the outer race for high signal to noise ratio. Furthermore, a morphological filter is used to enhance the feature signal which is subsequently measured by Kolmogorov complexity measure. Both simulated signals and measured data show that this system is able to diagnose defects including abnormal surface roundness, waviness, misaligned races which are typical quality problems in bearing manufacturing lines.