Rotating Machinery Fault Diagnosis Using HHO-RSSD and RCGmvMAAPE

Rotating machinery has played an enormous role in industrial production, and its stable operation is related to whether production can proceed smoothly. At present, multichannel entropy-based methods are usually be adopted to analyze multichannel vibration signals. However, the collected signal may...

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Bibliographic Details
Published inShock and vibration Vol. 2022; pp. 1 - 34
Main Author Zhang, Ning
Format Journal Article
LanguageEnglish
Published Cairo Hindawi 2022
John Wiley & Sons, Inc
Hindawi Limited
Subjects
Algorithms
Analysis
Belief networks
Classification
Decomposition
Deep learning
Efficiency
Entropy
Fault diagnosis
Machinery
Methods
Multivariate analysis
Optimization
Pattern recognition
Permutations
Resonance
Rotating machinery
Support vector machines
Time series
Vibration analysis
Wavelet transforms
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Summary:Rotating machinery has played an enormous role in industrial production, and its stable operation is related to whether production can proceed smoothly. At present, multichannel entropy-based methods are usually be adopted to analyze multichannel vibration signals. However, the collected signal may only have one channel in the actual situation. At this time, analyzing only a single channel signal cannot effectively utilize the advantages of multivariate analysis. For this reason, this paper presents a novel multivariate analysis approach and applies it to the fault diagnosis of machinery. Firstly, the parameter-optimized resonance sparse decomposition (RSSD) algorithm is adopted to decompose the single-channel vibration signal into high and low resonance components. Then, the two components are regarded as dual-channel vibration signals and input into the refined composite generalized multivariate multiscale amplitude aware permutation entropy (RCGmvMAAPE) method to gain fault features. Eventually, the features are input to the deep belief network (DBN) classifier to perform fault judgment. The experiments of rotating machinery are carried to verify the effectiveness of the developed approach. The results display that the proposed fault diagnosis method can achieve the classification accuracy of 100% and 98% when only a single-channel vibration signal is used, which is better than the fault diagnosis method based on a multichannel vibration signal and enjoys strong stability.
ISSN:1070-9622
1875-9203
DOI:10.1155/2022/6266671