Cyclostationary correntropy: Definition and applications

Fontes, Aluisio I. R.; Rego, Joilson B. A.; Martins, Allan de Medeiros; Silveira, Luiz F. Q.; Prı́ncipe, José C.

doi:10.1016/j.eswa.2016.10.029

Cited by 39 publications

(30 citation statements)

References 16 publications

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“…Therefore, the diagnosis performance of cyclic correntropy is largely determined by the kernel size. However, in previous studies related to cyclic correntropy, the selection method of kernel size was not addressed in detail [ 21 , 22 , 23 ] and kernel size mainly was decided by multiple trials. Thus, it is necessary to find a solution for the kernel size selection.…”

Section: Cyclostationary Analysis Based On Correntropy Functionmentioning

confidence: 99%

“…Recently, a new cyclostationary analysis technology named cyclic correntropy (CCE) analysis has emerged to suppress impulsive noise [ 21 , 22 , 23 ]. CCE is a kernel-based similarity measure of cyclostationary modeling signals.…”

Section: Introductionmentioning

confidence: 99%

“…CCE is a kernel-based similarity measure of cyclostationary modeling signals. Related research has shown that it has a good suppression performance when dealing with the binary phase-shift keying signal under an impulsive noise environment [ 21 , 23 ]. However, to the best of our knowledge, only several works related to CCE analysis have been published in the field of communication and its applications need to be developed and reinforced.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Zhao

Qin

et al. 2019

Entropy

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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.

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Section: Cyclostationary Analysis Based On Correntropy Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhao

Qin

et al. 2019

Entropy

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“…Recently, a generalized cyclic correlation analysis technique named cyclic correntropy (CCE) analysis was proposed to address the problem of impulsive noise [31]- [33]. Combining the cyclostationary modeling and correntropy theory, this technique performed well when processing the communication signals in the presence of impulsive noise.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Identification for Rolling Element Bearings in Impulsive Noise Environments Based on Cyclic Correntropy Spectra and LSSVM

Zhao

Qin

et al. 2020

IEEE Access

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Rolling element bearings are important components in various types of industrial equipment. It is necessary to develop advanced fault diagnosis techniques to prevent unexpected accidents caused by bearing failures. However, impulsive background noise in industrial fields also presents a similar fault-excited characteristic, which brings interference to the fault diagnosis of rolling element bearings. Focusing on this issue, this paper proposes a new feature extraction method based on the cyclic correntropy spectrum (CCES) for intelligent fault identification.. In this study, the cyclic correntropy (CCE) function is introduced to suppress the impulsive noise. A simplified frequency spectrum named CCES is obtained for the feature extraction. Then, narrowband kurtosis vectors are extracted from the CCES. Finally, these extracted features are used to train the least squares support vector machine (LSSVM) for the fault pattern identification. Analyses of two bearing datasets, including train axle bearing data that are contaminated by impulsive noise are used as case studies for the validation of the proposed method. To illustrate the advancement of the new method, performance comparisons with two recently developed methods are conducted. The experimental results verify that the proposed method not only outperforms these two methods but also exhibits a stable self-adaptation ability.

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“…nal processing applications (Liu et al, 2007;Fontes et al, 2014Fontes et al, , 2015Fontes et al, , 2017. In particular, the maximum correntropy criterion (MCC) has been successfully used in filtering, robust regression, and signal processing applications.…”

mentioning

confidence: 99%

Complex correntropy function: Properties, and application to a channel equalization problem

Guimarães

Fontes

Rego

et al. 2018

Expert Systems with Applications

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The use of correntropy as a similarity measure has been increasing in different scenarios due to the well-known ability to extract high-order statistic information from data. Recently, a new similarity measure between complex random variables was defined and called complex correntropy. Based on a Gaussian kernel, it extends the benefits of correntropy to complex-valued data. However, its properties have not yet been formalized. This paper studies the properties of this new similarity measure and extends this definition to positive-definite kernels. Complex correntropy is applied to a channel equalization problem as good results are achieved when compared with other algorithms such as the complex least mean square (CLMS), complex recursive least squares (CRLS), and least absolute deviation (LAD).

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Cyclostationary correntropy: Definition and applications

Cited by 39 publications

References 16 publications

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

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

Intelligent Fault Identification for Rolling Element Bearings in Impulsive Noise Environments Based on Cyclic Correntropy Spectra and LSSVM

Complex correntropy function: Properties, and application to a channel equalization problem

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