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- Title
2316. Fault diagnosis of rolling bearing based on improved CEEMDAN and distance evaluation technique.
- Authors
Feng Ding; Xiang Li; Jinxiu Qu
- Abstract
In order to accurately identify the fault conditions of rolling bearing, this paper presents a fault diagnosis method based on improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and distance evaluation technique. In this method, to effectively extract potential fault-related information, vibration signals of rolling bearing in different fault conditions are decomposed into a set of intrinsic mode functions (IMFs) through improved CEEMDAN. The first eight IMFs containing most fault information are selected for extracting fault features. The original feature set is obtained including energy values, singular values and envelope sample entropy values. Then distance evaluation technique is implemented for selecting sensitive feature set and discarding irrelevant or redundant features. Subsequently, the sensitive feature set is fed into support vector machine (SVM) for automatically identifying rolling bearing fault conditions. The simulation results demonstrate that improved CEEMDAN is able to solve the problem of mode mixing and achieve a numerically negligible reconstruction error. Meanwhile experimental consequences indicate that the proposed method can acquire higher identification accuracy, as well as reduce the classifier computational burden.
- Subjects
ROLLER bearing fracture; SURFACE cracks; HILBERT-Huang transform; SUPPORT vector machines; MACHINERY maintenance &; repair
- Publication
Journal of Vibroengineering, 2017, Vol 19, Issue 1, p260
- ISSN
1392-8716
- Publication type
Article
- DOI
10.21595/jve.2016.17398