Rolling Bearing Fault Diagnosis Method Based on Multisynchrosqueezing S Transform and Faster Dictionary Learning

Addressing the problem that it is difficult to extract the features of vibration signal and diagnose the fault of rolling bearing, we propose a novel diagnosis method combining multisynchrosqueezing S transform and faster dictionary learning (MSSST-FDL). Firstly, MSSST is adopted to transform vibrat...

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Vydané v:Shock and vibration Ročník 2021; číslo 1
Hlavní autori: Sun, Guodong, Hu, Ye, Wu, Bo, Zhou, Hongyu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cairo Hindawi 2021
John Wiley & Sons, Inc
Wiley
Predmet:
Algorithms
Analysis
Bearings
Coding
Datasets
Dictionaries
Energy
Failure prevention
Fault diagnosis
Feature extraction
Image resolution
Learning
Linear equations
Loudspeakers
Machine learning
Machinery
Mathematical analysis
Matrix methods
Methods
Neural networks
Object recognition
Roller bearings
Texture recognition
Transformations (mathematics)
Vibration
Wavelet transforms
China
ISSN:1070-9622, 1875-9203
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Popis
Shrnutí:Addressing the problem that it is difficult to extract the features of vibration signal and diagnose the fault of rolling bearing, we propose a novel diagnosis method combining multisynchrosqueezing S transform and faster dictionary learning (MSSST-FDL). Firstly, MSSST is adopted to transform vibration signals into high-resolution time-frequency images. Then, the local binary pattern (LBP) operator is introduced to extract the low-dimensional texture features of time-frequency images, which improves the speed of fault recognition. Finally, nonnegative matrix factorization (NMF) with only one hyperparameter and nonnegative linear equation are used to solve the dictionary learning and feature coding, respectively. The feature coding is input into the classifier for training and recognition. Experiments show that our method performs well on the rolling bearing dataset of Case Western Reserve University (CWRU) and the Society for Machinery Failure Prevention Technology (MFPT). Further, the proposed method is applied to the loudspeaker pure-tone detection dataset, and the loudspeaker anomaly diagnosis is achieved. The diagnosis results verify that our method can meet the needs of practical engineering.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1070-9622
1875-9203
DOI:10.1155/2021/8456991