A novel denoising algorithm based on TVF-EMD and its application in fault classification of rotating machinery. (July 2021)

• Record Type: Journal Article
• Authors: Zhang, Shuqing; Xu, Fengjiao; Hu, Mengfei; Zhang, Liguo; Liu, Haitao; Li, Mingliang
• Is Part Of: Measurement. Volume 179(2021)
• Publication Date: 2021-07
• Subjects: TVF-EMD -- EIMFs screening -- Feature matrix construction -- Signal denoising -- Mechanical fault classification; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109337
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: This paper proposes a novel algorithm for screening EIMFs. This paper applies EIMFs to signal denoising and feature matrix construction. This algorithm improves the problem of TVF-EMD to decompose too many imfs . This algorithm expands the application of TVF-EMD in the field of fault diagnosis. Abstract: This paper proposes a new narrow-band filtering algorithm to improve the problem of TVF-EMD algorithm decomposing too many narrow-bands. The algorithm uses the energy estimation model of IMFs combined with the energy of noise in each imf and the signal complexity evaluation standard to obtain the effectiveness operator that measures the signal content of each imf, and selects the eimf with a large effectiveness operator as the EIMFs. In this paper, three groups of rotating machine data are used for experiments. The classification accuracy of denoising signals can reach 99.98% when the effectiveness operator is accumulated to 0.9999, and the classification accuracy of the EIMFs feature matrix can reach 97.83%, which are higher than the original data control group. The algorithm only needs to deal with the advantages of EIMFs, which significantly improves the classification accuracy and iteration speed of the classifier.