An Efficient Method for Wheel-Flattened Defects Detection Based on Acoustic Emission Technique

Recent research studies show that acoustic emission (AE) technologies have great potential in detecting the flattened defects of wheel; however, the wheel-rail rolling interference (WRRI) significantly influences the accuracy of wheel defect detection. Aiming to solve the above-mentioned problem, a novel detection method, which combines an improved synthesized health index (ISHI) with a time adaptive threshold (time-ATH), is proposed in this article. In order to obtain the defect information from the signals generated by the wheels, a completed feature set that contains many types of features is extracted from AE signals. Then, the ISHI is fused by several effective features that are selected from the completed feature set, according to detection rate and accuracy. Besides, a time-ATH calculation is proposed to detect the defective signals of wheels and reduce the influence of the WRRI. The method is fully verified in actual datasets, and the results show that the proposed method achieves a better detection rate and accuracy. Moreover, it provides an effective way for the AE detection of wheel-flattened defects under strong and numerous WRRI.