A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree

Li, Yongbo; Xu, Min; Yu, Wei; Huang, Wenhu

doi:10.1016/j.measurement.2015.08.034

Cited by 253 publications

(150 citation statements)

References 29 publications

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“…The process of MPE is briefly described here. For more detailed information about MPE, it can be referred to in [33,35].…”

Section: Optimized Dmd Modes Via Mpementioning

confidence: 99%

Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy

Dang

et al. 2018

Entropy

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Dynamic mode decomposition (DMD) is essentially a hybrid algorithm based on mode decomposition and singular value decomposition, and it inevitably inherits the drawbacks of these two algorithms, including the selection strategy of truncated rank order and wanted mode components. A novel denoising and feature extraction algorithm for multi-component coupled noisy mechanical signals is proposed based on the standard DMD algorithm, which provides a new method solving the two intractable problems above. Firstly, a sparse optimization method of non-convex penalty function is adopted to determine the optimal dimensionality reduction space in the process of DMD, obtaining a series of optimal DMD modes. Then, multiscale permutation entropy calculation is performed to calculate the complexity of each DMD mode. Modes corresponding to the noise components are discarded by threshold technology, and we reconstruct the modes whose entropies are smaller than a threshold to recover the signal. By applying the algorithm to rolling bearing simulation signals and comparing with the result of wavelet transform, the effectiveness of the proposed method can be verified. Finally, the proposed method is applied to the experimental rolling bearing signals. Results demonstrated that the proposed approach has a good application prospect in noise reduction and fault feature extraction.

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“…The process of MPE is briefly described here. For more detailed information about MPE, it can be referred to in [33,35].…”

Section: Optimized Dmd Modes Via Mpementioning

confidence: 99%

Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy

Dang

et al. 2018

Entropy

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“…More recent references of SVM in fault diagnosis can be found in Refs. [81][82][83][84][85][86][87][88].…”

Section: Svmmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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“…Therefore, the feature extraction and pattern recognition of bearings diagnosis are very important. The time-frequency analysis method has been widely used in faults diagnosis, because it can provide the information in the time and frequency domain [2]. Moreover, there are many methods of artificial intelligence detection, such as statistical processing to sense [3], stray magnetic flux measurement [4], and neural networks such as support vector machine (SVM) [2].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis

Gao

Villecco

et al. 2017

Entropy

140

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Based on the combination of improved Local Mean Decomposition (LMD), Multi-scale Permutation Entropy (MPE) and Hidden Markov Model (HMM), the fault types of bearings are diagnosed. Improved LMD is proposed based on the self-similarity of roller bearing vibration signal by extending the right and left side of the original signal to suppress its edge effect. First, the vibration signals of the rolling bearing are decomposed into several product function (PF) components by improved LMD respectively. Then, the phase space reconstruction of the PF1 is carried out by using the mutual information (MI) method and the false nearest neighbor (FNN) method to calculate the delay time and the embedding dimension, and then the scale is set to obtain the MPE of PF1. After that, the MPE features of rolling bearings are extracted. Finally, the features of MPE are used as HMM training and diagnosis. The experimental results show that the proposed method can effectively identify the different faults of the rolling bearing.

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A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree

Cited by 253 publications

References 29 publications

Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy

Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis

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