Bearing Defect Detection Based on Acoustic Feature Extraction and Statistical Learning

Bearings are widely utilized as key components in industrial scenarios. Therefore, the automatic and precise inspection of bearing defects is imperative for the manufacturing of the bearing. In this paper, a novel defect detection method based on acoustics is proposed to further improve both the accuracy and the efficiency of the defection process. We firstly constructed a labeled dataset composed of acoustic signals sampling from different bearings with a certain rotational speed. OpenSMILE is adopted to extract the acoustic features and the target acoustic feature dataset with 6373 features is formed. To further improve the efficiency of the proposed method, a feature selection strategy based on the chi-square test is adopted to eliminate the most inefficient features. Several statistical learning models are constructed and trained as the classifier. Eventually, the performance of classifiers is evaluated and achieves relatively high accuracy and efficiency with an extremely imbalanced dataset.