Rolling Element Bearing Fault Diagnosis under Impulsive Noise Environment Based on Cyclic Correntropy Spectrum

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.

Selection of the Informative Frequency Band in a Bearing Fault Diagnosis in the Presence of Non-Gaussian Noise—Comparison of Recently Developed Methods

“…In the classical approach of cyclostationary-based techniques the assumption of Gaussian noise is taken under consideration [7,9,[22][23][24][25][26]. However, one can find the papers where the algorithms for the signals with non-Gaussian noise are proposed [27][28][29][30][31][32][33][34][35][36][37][38]. We refer also to the recent paper, where the new definition of the cyclostationary non-Gaussian signal is given, see [27] for more details.…”

Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO

“…Rolling bearings are a crucial part in modern industrial manufacture, which can be found in linear guides, precision machine tools, and engine parts, etc., whose failure may result in serious safety accidents and economic loss. Therefore, one of the major topics to be investigated in preventing failures of mechanical systems is recognizing and diagnosing rolling bearing faults [1,2]. Unfortunately, rolling bearings are prone to failure to some extent because of complex operating conditions and structural characteristics [3].…”

“…It can be seen that all the time series are mapped to the same dispersion pattern, although their trends are different. Meanwhile, the time series are mapped by the proposed FSDE, factor f is added as an additional element to the end of the class sequence, the new class sequences are[4,4,4,4, 0],[4,4,4,4, 1], and[4,4,4,4,2]. In the example, the time series originally mapped to the same dispersion pattern are finely mapped to more dispersion patterns.Figure 2illustrates the way that f is added to the sequence ,…”

Practical Stability with Respect to h-Manifolds for Impulsive Control Functional Differential Equations with Variable Impulsive Perturbations

“…Impulsive control problems are well described by impulsive differential systems. Due to their numerous applications in various fields, such as engineering [1][2][3], biotechnologies and ecosystems management [4], chemical kinetics [5], and finance and investment [6,7], such systems have been extensively studied in past years. See, for example, [8,9] and the references therein for some fundamental results on impulsive differential equations and related impulsive control models.…”