Defect detection in freight railcar tapered-roller bearings using vibration techniques

Currently,there are two types of defect detection systems used to monitor the health of freight railcar bearings in service:wayside hot-box detection systems and trackside acoustic detection systems.These systems have proven to be inefficient in accurately determining bearing health,especially in the early stages of defect development.To that end,a prototype onboard bearing condition monitoring system has been developed and validated through extensive laboratory testing and a designated field test in 2015 at the Transportation Technology Center,Inc.in Pueblo,CO.The devised system can accurately and reliably characterize the health of bearings based on developed vibration thresholds and can identify defective taperedroller bearing components with defect areas smaller than 12.9 cm2 while in service.

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.