A Diagnosis Method of Cleanliness Defect in Rolling Element Bearing Manufacturing

The cleanliness defect is commonly-occurred during the manufacturing process of rolling element bearings, which reduces the defect detection accuracy and further deteriorates the noise vibration harshness of bearings. To effectively detect the cleanliness defect of ex-factory bearings, this paper presents a method based on the sliding window kurtosis indicator. It first calculates the distribution of kurtosis and the threshold-overshoot sum of the sliding window kurtosis series to assess the degree of cleanliness. After that, an indicator-sensitive parameters and the judgment threshold are adaptively selected by a Bayesian optimization algorithm. The proposed method is verified with data from the bearing manufacturing processing. The results show that the proposed method has high accuracy for identifying cleanliness of bearings.