Underdetermined blind source extraction of early vehicle bearing faults based on EMD and kernelized correlation maximiza
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Underdetermined blind source extraction of early vehicle bearing faults based on EMD and kernelized correlation maximization Xuejun Zhao1 · Yong Qin1,2,3
· Changbo He4 · Limin Jia1,2,3
Received: 4 May 2020 / Accepted: 18 August 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The incipient bearing fault diagnosis is crucial to the industrial machinery maintenance. Developed based on the blind source separation, blind source extraction (BSE) has recently become the focus of intensive research work. However, owing to certain industrial restrictions, the number of sensors is usually less than that of the source signals, which is defined as an underdetermined BSE problem to identify the fault signals. The kernelized methods are found to be robust to the noise, especially in the presence of outliers, which makes it a suitable tool to extract fault signatures submerged in the strong environment noise. Thus, this paper proposes a new underdetermined BSE method based on the empirical mean decomposition and kernelized correlation. The experimental results indicate that the extracted fault signature presents more obvious periodicity. Two important parameters of this method, including the multi-shift number and the kernel size are investigated to improve the algorithm performance. Furthermore, performance comparisons with underdetermined BSE based on the second order correlation are made to emphasize the advantage of the presented method. The application of the proposed method is validated using the simulated signal and the rolling element bearing signal of the train vehicle axle. Keywords Blind source extraction · Signal decomposition · Kernelized correlation · Fault diagnosis
Introduction Rolling element bearings are one of the most widely used mechanical components in various industrial machines, such as gearboxes, train vehicle axles and turbines. Their health states tend to degrade due to repeating rotations under harsh working conditions. The early bearing fault detection and diagnosis is crucial to prevent the machine breakdown and ensure the production efficiency. Therefore, research related to bearing degradation prognostics and early fault diagno-
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Yong Qin [email protected]
1
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
2
Beijing Research Center of Urban Traffic Information Sensing and Service Technologies, Beijing Jiaotong University, Beijing 100044, China
3
National Engineering Laboratory for System Safety and Operation Assurance of Urban Rail Transit, Guangzhou 510000, China
4
College of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
sis has attracted extensive attention recently (Li et al. 2015, 2017; Wang et al. 2017; Wang and Tsui 2018). Blind source separation (BSS) is one of many interesting branches among the early fault diagnosis research. Since Gelle et al. (2001, 2003) introduced the BSS to the fault diagnosis of the rotating machinery, more and more scholars have
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