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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.

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Gear transmission is a key component in locomotive where it delivers the traction or braking forces between the motor and the wheelset. Its working performance has a direct effect on the operating reliability and safety. Therefore, investigation on the dynamic characteristics of the gear transmission in locomotives is very meaningful. In this study, a gear transmission-locomotive-track spatial coupled dynamic model is established based on the classical locomotive-track coupled dynamics and the gear dynamics theory. Based on this model, the dynamic responses of the gear transmission can be analysed under excitations from different track geometrical irregularity, and the dynamic performance of the gear transmission can be obtained. This paper also studies the effect law of the track irregularity on the vibration of the gear transmission by using statistical indicators RMS (Root Mean Square) and PtP (Peak-to-Peak). The results indicate that the track geometrical irregularity has an obvious impact to the dynamic performance of gear transmission. The dynamic response of the gear transmission will increase violently when the locomotive runs on the track in a worse condition. The results are expected to be capable of providing some references for fatigue life prediction and reliability analysis of the gear transmissions in locomotive.

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