Abstract
A wheelset bearing is a crucial energy transmission element in high-speed trains. Any parts of the wheelset bearing that have faults may endanger the safety of the railway service. Therefore, it is important to monitor the running condition of a wheelset bearing. The multifault on a wheelset bearing is very common, and these impulsive components generated by different types of faults may interact with each other, which increases the difficulty of entirely identifying those faults. To solve the multifault problem, this paper proposed a hierarchical shift-invariant K-means singular value decomposition (H-SI-K-SVD) to hierarchically separate those multifault impulsive components based on their fault power levels. Each of the separated impulse signals contains only one fault impulse, and the fault information could be highlighted both in time domain and frequency domain. In addition, the sparsity of envelope spectrum (SES) is introduced as an indicator to adaptively tune a key parameter in this method. The effectiveness of the proposed method is verified by both simulation and experimental signals. Compared with ensemble empirical model decomposition (EEMD), the proposed method exhibits better performance in separating the multifault impulsive components and detecting the faults of a wheelset bearing.
Highlights
A wheelset, as an important part of a high-speed train, mainly consists of three types of components: an axle, two wheels, and some wheelset bearings
On the basis of the aforementioned SI-K-singular value decomposition (SVD), this paper propose hierarchical SI-K-SVD (H-SI-K-SVD) to hierarchically separate the wheelset bearing multifault impulsive components based on their power levels of the impulse signals and each of the separated impulse signals only contains only one fault impulse, which could be employed to detect the multifault of a wheelset bearing
On the basis of this idea, this paper proposed H-SI-K-SVD to hierarchically separate the wheelset bearing multifault impulsive components based on their power levels of those impulse signals
Summary
A wheelset, as an important part of a high-speed train, mainly consists of three types of components: an axle, two wheels, and some wheelset bearings. Compared with traditional time-domain statistical indicators and transform-based methods, dictionary learning is a data-driven method and can adaptively match the fault feature information from the measured vibration signals [19, 20]. Erefore, shift-invariant dictionaries are very helpful both in extracting these latent similar components generated by the faults from the measured vibration signal and monitoring the wheelset bearings running condition [22, 23]. The fault detection of a wheelset bearing employing this method is more difficult because of severe background noise and other types of interference, such as wheelset axle vibration and other structural vibration [28]. It is difficult to completely identify multifaults using this method, especially for the lower power impulse signals These problems render multifault impulsive component simultaneous separation and detection difficult using SI-K-SVD.
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