Fault diagnosis method of wheel diameter difference based on stacked autoencoder and multiple kernel learning

Abstract

Tread wear rates of the right and left wheels of a wheelset are not the same because of the complexity of the track condition, which causes the wheel diameter difference (WDD). The WDD can influence vehicle dynamic performances and shorten the service life of the wheelset. To diagnose and recognize the condition of the WDD in time, a data-driven method based on multi-sensor information fusion is proposed. Different statistical features are extracted from the time and frequency domains of the axle-box acceleration signals. The features can be fused by integrating stacked autoencoder and multiple kernel learning. The comparative experimental analysis shows that compared with other commonly used intelligent methods, the proposed method can achieve higher diagnostic accuracy and give better performance with small training sample sizes. The statistical features sensitive to the WDD are also analyzed for industrial application.