A Method for Extracting Fault Features Using Variable Multilevel Spectral Segmentation Framework and Harmonic Correlation Index

Adaptive intelligent fault diagnosis technology can improve the automatic status monitoring capability and the success rate of fault diagnosis. This article proposed an adaptive mode decomposition and noise reduction algorithm (VAMD) that use a variable spectral segmentation framework to optimize an...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2022, Vol.71, p.1-9
Main Authors: Zhang, Kun, Tang, Haihong, Chen, Peng, Xu, Yonggang, Hu, Aijun
Format: Article
Language:English
Subjects:
Algorithms
Analytical mode decomposition (AMD)
Bandwidth
Decomposition
Fault detection
Fault diagnosis
Feature extraction
Frequency modulation
Harmonic analysis
harmonic correlation index (HCI)
Indexes
Noise reduction
Power harmonic filters
Roller bearings
Rotating machinery
Segmentation
signal processing
variable spectral segmentation framework
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Summary:Adaptive intelligent fault diagnosis technology can improve the automatic status monitoring capability and the success rate of fault diagnosis. This article proposed an adaptive mode decomposition and noise reduction algorithm (VAMD) that use a variable spectral segmentation framework to optimize analytical mode decomposition (AMD) to automatically decompose the mode information in rotating machinery signals. The framework relies on the variability of the window width and envelope estimation characteristics of the order statistics filter (OSF) to increase the diversity of the center frequencies and bandwidth. A novel harmonic correlation index (HCI) is designed to identify the characteristics of rotating machinery faults from various levels of results and improve the usability in mechanical equipment fault diagnosis. The proposed method has successfully achieved fault diagnosis of rolling bearing.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2021.3136252