2021
DOI: 10.3390/en14061555
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Research on the Fault Feature Extraction of Rolling Bearings Based on SGMD-CS and the AdaBoost Framework

Abstract: Symplectic geometric mode decomposition (SGMD) is a newly proposed signal processing method. Because of its superiority, it has gained more and more attention in the field of fault diagnosis. However, the similar component reorganization problem involved in this method has not been clearly stated. Aiming at this problem, this paper proposes the SGMD-CS method based on the SGMD method and the cosine similarity (CS) and has been compared and verified on the simulation signal and the actual rolling bearing signal… Show more

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Cited by 15 publications
(11 citation statements)
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“…e window will slide with a step size of 1 pixel in the image and finally extracts a complete image [27]. Afterward, the sliding length or width will be increased to repeat the sliding process until the sliding window size reaches a threshold [28,29]. Figure 5 describes the data preprocessing flow.…”
Section: Target Recognition and Feature Extraction (Fe)mentioning
confidence: 99%
“…e window will slide with a step size of 1 pixel in the image and finally extracts a complete image [27]. Afterward, the sliding length or width will be increased to repeat the sliding process until the sliding window size reaches a threshold [28,29]. Figure 5 describes the data preprocessing flow.…”
Section: Target Recognition and Feature Extraction (Fe)mentioning
confidence: 99%
“…Literature [32] hybridized the AdaBoost with a linear support vector machine model and developed a diagnostic system to predict hepatitis disease; the results demonstrate that the strength of a conventional support vector machine model is improved by 6.39%. Literature [33] studied the use Neural Networks (…”
Section: Multiclass Adaboost Method Adaboostmentioning
confidence: 99%
“…The vibration signals of the wind turbine HSSB are decomposed by CEEMD, and the sum of a group of IMF components and residual terms are obtained [35][36][37]. The first n IMF energies are calculated as follows:…”
Section: Time-frequency Domain Featuresmentioning
confidence: 99%