Intelligent Diagnosis of Rolling Element Bearing Based on Refined Composite Multiscale Reverse Dispersion Entropy and Random Forest

Liu, Aiqiang; Yang, Zhenzhong; Li, Hongkun; Wang, Chaoge; Liu, Xuejun

doi:10.3390/s22052046

Cited by 14 publications

(3 citation statements)

References 47 publications

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“…So far, a lot of work has focused on regular fault feature extraction through various entropy methods [ 29 , 30 , 31 ] but how to optimize entropy methods to extract incipient fault features is still in the early phase. Therefore, the improvement of entropy methods to extract incipient fault features can be regarded as the future research direction; this research direction needs to consider characteristics of incipient fault signals to overcome problems of entropy methods in incipient fault sample processing.…”

Section: Discussionmentioning

confidence: 99%

An Improved Incipient Fault Diagnosis Method of Bearing Damage Based on Hierarchical Multi-Scale Reverse Dispersion Entropy

Xing

2022

Entropy

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The amplitudes of incipient fault signals are similar to health state signals, which increases the difficulty of incipient fault diagnosis. Multi-scale reverse dispersion entropy (MRDE) only considers difference information with low frequency range, which omits relatively obvious fault features with a higher frequency band. It decreases recognition accuracy. To defeat the shortcoming with MRDE and extract the obvious fault features of incipient faults simultaneously, an improved entropy named hierarchical multi-scale reverse dispersion entropy (HMRDE) is proposed to treat incipient fault data. Firstly, the signal is decomposed hierarchically by using the filter smoothing operator and average backward difference operator to obtain hierarchical nodes. The smoothing operator calculates the mean sample value and the average backward difference operator calculates the average deviation of sample values. The more layers, the higher the utilization rate of filter smoothing operator and average backward difference operator. Hierarchical nodes are obtained by these operators, and they can reflect the difference features in different frequency domains. Then, this difference feature is reflected with MRDE values of some hierarchical nodes more obviously. Finally, a variety of classifiers are selected to test the separability of incipient fault signals treated with HMRDE. Furthermore, the recognition accuracy of these classifiers illustrates that HMRDE can effectively deal with the problem that incipient fault signals cannot be easily recognized due to a similar amplitude dynamic.

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Section: Discussionmentioning

confidence: 99%

An Improved Incipient Fault Diagnosis Method of Bearing Damage Based on Hierarchical Multi-Scale Reverse Dispersion Entropy

Xing

2022

Entropy

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“…The proposed RCMDE not only solves the single-scale problem of DE, but also improves the stability of traditional coarse graining. Inspired by RCMDE, some scholars immediately proposed refined composite MFDE (RCMFDE) [43], refined composite RDE (RCMRDE) [44], and refined composite multiscale FRDE (RCMFRDE) [28], respectively. Referring to the experience that fine composite processing can effectively represent signal complexity, some scholars introduced multivariate theory based on refined composite multiscale processing, and proposed refined composite multiscale multivariate MDE (RCMMDE) [45] and refined composite multiscale multivariate FDE (RCMMFDE) [46], which have low sensitivity to signal length and high noise resistance.…”

Section: Improved De Algorithmmentioning

confidence: 99%

Feature extraction method of ship-radiated noise based on dispersion entropy: A review

2023

Front. Phys.

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There is abundant ship information in ship-radiated noise, which is helpful for ship target recognition, classification and tracking. However, owing to the increasing complexity of the marine environment, it makes difficult to extract S-RN features. Dispersion entropy has been proven to be an excellent method to extract the features of S-RN by analyzing the complexity of S-RN, and has been widely used in feature extraction of S-RN. This paper summarizes the research progress of DE in the feature extraction of S-RN in recent years, and provides a comprehensive reference for researchers related to this topic. First, DE and its improved algorithm are described. Then the traditional and DE-based S-RN feature extraction methods are summarized, and the application of DE in S-RN feature extraction methods is concluded from two aspects: methods that apply DE algorithms only and methods that combine DE with mode decomposition algorithms. Finally, the research prospects of DE and the summary of this paper are given.

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“…After feature extraction, suitable classification algorithms need to be utilized to discover various potential faults of rolling bearing in time, and a lot of research has been carried out by domestic and foreign scholars in machine learning and other aspects. Commonly used methods generally include Back Propagation (BP), Random Forest (RF), Support Vector Machine (SVM), Extreme Learning Machine (ELM), etc., which are implemented by mapping and matching fault features to classification labels [15][16][17][18]. Xiao et al [19] proposed a fault diagnosis model with a beetle-optimized BP neural network, which can find the error extremes faster, shorten the training time, and have some anti-interference capability.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method of Rolling Bearing Based on ESGMD-CC and AFSA-ELM

He,

Liu,

Geng

et al. 2024

SDHM

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Incomplete fault signal characteristics and ease of noise contamination are issues with the current rolling bearing early fault diagnostic methods, making it challenging to ensure the fault diagnosis accuracy and reliability. A novel approach integrating enhanced Symplectic geometry mode decomposition with cosine difference limitation and calculus operator (ESGMD-CC) and artificial fish swarm algorithm (AFSA) optimized extreme learning machine (ELM) is proposed in this paper to enhance the extraction capability of fault features and thus improve the accuracy of fault diagnosis. Firstly, SGMD decomposes the raw vibration signal into multiple Symplectic geometry components (SGCs). Secondly, the iterations are reset by the cosine difference limitation to effectively separate the redundant components from the representative components. Additionally, the calculus operator is performed to strengthen weak fault features and make them easier to extract, and the singular value decomposition (SVD) weighted by power spectrum entropy (PSE) can be utilized as the sample feature representation. Finally, AFSA iteratively optimized ELM is adopted as the optimized classifier for fault identification. The superior performance of the proposed method has been validated by various experiments.

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Intelligent Diagnosis of Rolling Element Bearing Based on Refined Composite Multiscale Reverse Dispersion Entropy and Random Forest

Cited by 14 publications

References 47 publications

An Improved Incipient Fault Diagnosis Method of Bearing Damage Based on Hierarchical Multi-Scale Reverse Dispersion Entropy

An Improved Incipient Fault Diagnosis Method of Bearing Damage Based on Hierarchical Multi-Scale Reverse Dispersion Entropy

Feature extraction method of ship-radiated noise based on dispersion entropy: A review

Fault Diagnosis Method of Rolling Bearing Based on ESGMD-CC and AFSA-ELM

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