Abstract
Rolling element bearings are widely used in various industrial machines. Fault diagnosis of rolling element bearings is a necessary tool to prevent any unexpected accidents and improve industrial efficiency. Although proved to be a powerful method in detecting the resonance band excited by faults, the spectral kurtosis (SK) exposes an obvious weakness in the case of impulsive background noise. To well process the bearing fault signal in the presence of impulsive noise, this paper proposes a fault diagnosis method based on the cyclic correntropy (CCE) function and its spectrum. Furthermore, an important parameter of CCE function, namely kernel size, is analyzed to emphasize its critical influence on the fault diagnosis performance. Finally, comparisons with the SK-based Fast Kurtogram are conducted to highlight the superiority of the proposed method. The experimental results show that the proposed method not only largely suppresses the impulsive noise, but also has a robust self-adaptation ability. The application of the proposed method is validated on a simulated signal and real data, including rolling element bearing data of a train axle.
Highlights
Rolling element bearings are one of the most widely used mechanical components in various industrial machines, such as gearboxes, railway axles, and turbines
This paper investigates the influence of kernel size on the cyclic correntropy (CCE)
Assume that the bearing fault signal is cyclostationary on the second order, which indicates that the instantaneous autocorrelation function is periodic with period T [20]: R x = E x x∗ = R x
Summary
Rolling element bearings are one of the most widely used mechanical components in various industrial machines, such as gearboxes, railway axles, and turbines. Fast Kurtogram returns the complex envelopes of the signal in selected frequency bands with an arborescent multi-rate filter-bank structure, reducing the computation complexity This SK-based algorithm has inspired many related works on the fault diagnosis of rotating machines [8,9,10]. This paper investigates the influence of kernel size on the CCE performance, which is critical in the CCE analysis This is proven to be a promising fault diagnosis method for bearings as well as other rotating machinery in the presence of impulsive noise. A cyclostationary analysis method based on the correntropy function is introduced into the fault diagnosis of the rolling element bearing under an impulsive noise environment. The diagnosis performance of the proposed method is compared with the Fast Kurtogram method using train axle bearing data to verify its suitability
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