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Feature Extraction of Impulse Faults for Vibration Signals Based on Sparse Non-Negative Tensor Factorization
- Source :
- Applied Sciences, Vol 9, Iss 18, p 3642 (2019), Applied Sciences, Volume 9, Issue 18
- Publication Year :
- 2019
- Publisher :
- MDPI AG, 2019.
-
Abstract
- The incipient damages of mechanical equipment excite weak impulse vibration, which is hidden, almost unobservable, in the collected signal, making fault detection and failure prevention at the inchoate stage rather challenging. Traditional feature extraction techniques, such as bandpass filtering and time-frequency analysis, are suitable for matrix processing but challenged by the higher-order data. To tackle these problems, a novel method of impulse feature extraction for vibration signals, based on sparse non-negative tensor factorization is presented in this paper. Primarily, the phase space reconstruction and the short time Fourier transform are successively employed to convert the original signal into time-frequency distributions, which are further arranged into a three-way tensor to obtain a time-frequency multi-aspect array. The tensor is decomposed by sparse non-negative tensor factorization via hierarchical alternating least squares algorithm, after which the latent components are reconstructed from the factors by the inverse short time Fourier transform and eventually help extract the impulse feature through envelope analysis. For performance verification, the experimental analysis on the bearing datasets and the swashplate piston pump has confirmed the effectiveness of the proposed method. Comparisons to the traditional methods, including maximum correlated kurtosis deconvolution, singular value decomposition, and maximum spectrum kurtosis, also suggest its better performance of feature extraction.
- Subjects :
- Computer science
Feature extraction
02 engineering and technology
Impulse (physics)
lcsh:Technology
Fault detection and isolation
impulse fault
phase space reconstruction
time-frequency distribution
lcsh:Chemistry
Singular value decomposition
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
sparse non-negative tensor factorization
Instrumentation
lcsh:QH301-705.5
Fluid Flow and Transfer Processes
lcsh:T
Process Chemistry and Technology
feature extraction
020208 electrical & electronic engineering
General Engineering
Short-time Fourier transform
020206 networking & telecommunications
lcsh:QC1-999
Computer Science Applications
Vibration
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
Kurtosis
Deconvolution
lcsh:Engineering (General). Civil engineering (General)
Algorithm
lcsh:Physics
Subjects
Details
- Language :
- English
- ISSN :
- 20763417
- Volume :
- 9
- Issue :
- 18
- Database :
- OpenAIRE
- Journal :
- Applied Sciences
- Accession number :
- edsair.doi.dedup.....0b137d396d2e6e02dfc3260684464df8