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      Multi-objective Informative Frequency Band Selection Based on Negentropy-induced Grey Wolf Optimizer for Fault Diagnosis of Rolling Element Bearings

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          Abstract

          Informative frequency band (IFB) selection is a challenging task in envelope analysis for the localized fault detection of rolling element bearings. In previous studies, it was often conducted with a single indicator, such as kurtosis, etc., to guide the automatic selection. However, in some cases, it is difficult for that to fully depict and balance the fault characters from impulsiveness and cyclostationarity of the repetitive transients. To solve this problem, a novel negentropy-induced multi-objective optimized wavelet filter is proposed in this paper. The wavelet parameters are determined by a grey wolf optimizer with two independent objective functions i.e., maximizing the negentropy of squared envelope and squared envelope spectrum to capture impulsiveness and cyclostationarity, respectively. Subsequently, the average negentropy is utilized in identifying the IFB from the obtained Pareto set, which are non-dominated by other solutions to balance the impulsive and cyclostationary features and eliminate the background noise. Two cases of real vibration signals with slight bearing faults are applied in order to evaluate the performance of the proposed methodology, and the results demonstrate its effectiveness over some fast and optimal filtering methods. In addition, its stability in tracking the IFB is also tested by a case of condition monitoring data sets.

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          Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization

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            Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics

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              The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                26 March 2020
                April 2020
                : 20
                : 7
                : 1845
                Affiliations
                State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China; yangsp@ 123456stdu.edu.cn (S.Y.); liuyq@ 123456stdu.edu.cn (Y.L.); haorj@ 123456stdu.edu.cn (R.H.); cap_0219@ 123456163.com (Z.L.)
                Author notes
                [* ]Correspondence: guxh@ 123456stdu.edu.cn ; Tel.: +86-0311-8793-6430
                Author information
                https://orcid.org/0000-0001-7114-0797
                Article
                sensors-20-01845
                10.3390/s20071845
                7181273
                32225091
                bc3a7a04-5408-47e4-bf95-27660aa4ae4e
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 14 February 2020
                : 25 March 2020
                Categories
                Article

                Biomedical engineering
                bearing,fault diagnosis,multi-objective optimization,grey wolf optimizer,wavelet filter,negentropy

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