Abstract
A railway vehicle’s key components, such as wheelset treads and axle box bearings, often suffer from fatigue failures. If these faults are not detected and dealt with in time, the running safety of the railway vehicle will be seriously affected. To detect these components’ early failure and extend their fatigue life, a regular maintenance becomes critical. Currently, the regular maintenance of axle box bearings mainly depends on manual off‐line inspection, which has low working efficiency and precision of fault diagnosis. In order to improve the maintenance efficiency and effectiveness of railway vehicles, this study proposes a method of integrating the vibration monitoring system of the axle box bearing in the underfloor wheelset lathe, where the integration scheme and work flow of the system are introduced followed by the detailed fault diagnosis method and application examples. Firstly, the band‐pass filter and envelope analysis is successively performed on the original signal acquired by an accelerometer. Secondly, the alpha‐stable distribution (ASD) and multifractal detrended fluctuation analysis (MFDFA) analysis of the envelope signal are performed, and five characteristic parameters with significant stability and sensitivity are extracted and then brought into the least squares support vectors machine based on particle swarm optimization to determine the state of the bearing quantitatively. Finally, the effectiveness of the method is validated by bench test data. The results demonstrated that the proposed method can accomplish effective diagnosis of axle box bearings’ fault location and fault degree and can yield better diagnosis accuracy than the single method of ASD or MFDFA.
Highlights
A railway vehicle is usually subjected to complex conditions with variable speeds, loading, and temperatures, which unavoidably lead to wear, pitting, and even peeling fault of its core components, such as wheel treads and axle box bearings (Figure 1) [1,2,3]. ese failures will reduce its ride comfort and service life and impair its safety that may cause serious economic losses or catastrophic casualties, as well as the formation of negative social influence
In References [17, 18], we have studied the sensitivity and stability on fault degrees of the characteristic parameters extracted by alpha-stable distribution (ASD) and multifractal detrended fluctuation analysis (MFDFA), respectively. e results show that the three ASD parameters and the two MFDFA parameters have excellent sensitivity and stability and can be used as the fault characteristics to distinguish the rolling bearings’ faults with different locations and degrees
(3) Fault Diagnosis. e ASD and MFDFA analyses of the envelope signal are carried out, and five fault characteristic parameters (α, c, H, hq0, and hqmin) are extracted into the PSO-LSSVM model which has been well trained to determine the state of the bearing quantitatively
Summary
A railway vehicle is usually subjected to complex conditions with variable speeds, loading, and temperatures, which unavoidably lead to wear, pitting, and even peeling fault of its core components, such as wheel treads and axle box bearings (Figure 1) [1,2,3]. ese failures will reduce its ride comfort and service life and impair its safety that may cause serious economic losses or catastrophic casualties, as well as the formation of negative social influence. Without changing the original structure and function of UWL, this method can diagnose the common faults of axle box bearings It is different from the current working mode that UWL can only deal with tread damage, and it is of great significance to improve and enrich the existing vehicle maintenance strategy. The installation of the fault diagnosis system of axle box bearings on UWL has definite engineering background and wide application prospect It increases a maintenance opportunity of bearings which is beneficial to the early detection and prediction of the bearings’ faults more accurately and improves the operation efficiency of the railway vehicle and the economic benefits of the railway business
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