Research on Fault Feature Extraction of Rolling Bearing Based on dFIF

Abstract

Aiming at the problems that the traditional adaptive mode decomposition method has low decomposition efficiency, poor ability of anti-mode mixing and decomposition accuracy, a rolling bearing fault feature recognition method based on direct fast iterative filtering (dFIF) and the crest factor of envelope spectrum is proposed. dFIF first decomposes the raw rolling bearing vibration signal into a set of intrinsic mode function (IMFs), then the optimal component is chosen based on the maximal crest factor of envelope spectrum, and finally the bearing fault characteristic frequency and its frequency multiplication are extracted using envelope demodulation analysis. It is demonstrated by relevant experimental data that the proposed method has efficient decomposition speed, accurate decomposition accuracy, and the accurate selection of effective components, and can effectively realize rolling bearing fault detection.

A systematic literature review of knowledge graph construction and application in education

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New intelligent fault diagnosis approach of rolling bearing based on improved vibration gray texture image and vision transformer

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