Cross‐domain bearing fault diagnosis with refined composite multiscale fuzzy entropy and the self organizing fuzzy classifier

Gituku, Esther Wangui; Kimotho, James Kuria; Njiri, Jackson G.

doi:10.1002/eng2.12307

Cited by 7 publications

(4 citation statements)

References 32 publications

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“…TFCF+RTFANet (Proposed) 3 99.86 FRFT+SSA-DBN [4] 3 95 STFT+CNN [32] 3 96 WPT-MWSVD+SVM [5] 3 87.8 CNN-BLSTM [30] 3 99.2 ResNet-STAC-tanh [31] 3 90.77 RCMFE+SOF [6] 3 95.8…”

Section: Methods Fault Types Accuracy (%)mentioning

confidence: 99%

“…Finally, the extracted feature matrix is used as the input of the support vector machine (SVM) classifier for bearing fault diagnosis. Gituku et al [6] used refined composite multiscale fuzzy entropy (RCMFE) for cross-domain diagnosis of bearing faults and used self-organizing fuzzy (SOF) classifier for classification. Although these methods are easy to implement, the limitations of Heisenberg's uncertainty principle [7] prevent them from improving time and frequency resolution.…”

Section: Introductionmentioning

confidence: 99%

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Rolling Bearing Fault Diagnosis Based on Time-Frequency Compression Fusion and Residual Time-Frequency Mixed Attention Network

et al. 2022

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The traditional rolling bearing diagnosis algorithms have problems such as insufficient information on time-frequency images and poor feature extraction ability of the diagnosis model. These problems limit the improvement of diagnosis performance. In this article, the input of the time-frequency image and intelligent diagnosis algorithms are optimized. Firstly, the characteristics of two advanced time-frequency analysis algorithms are deeply analyzed, i.e., multisynchrosqueezing transform (MSST) and time-reassigned multisynchrosqueezing transform (TMSST). Then, we propose time-frequency compression fusion (TFCF) and a residual time-frequency mixed attention network (RTFANet). Among them, TFCF superposes and splices two time-frequency images to form dual-channel images, which can fully play the characteristics of multi-channel feature fusion of the convolutional kernel in the convolutional neural network. RTFANet assigns attention weight to the channels, time and frequency of time-frequency images, making the model pay attention to crucial time-frequency information. Meanwhile, the residual connection is introduced in the process of attention weight distribution to reduce the information loss of feature mapping. Experimental results show that the method converges after seven epochs, with a fast convergence rate and a recognition rate of 99.86%. Compared with other methods, the proposed method has better robustness and precision.

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“…TFCF+RTFANet (Proposed) 3 99.86 FRFT+SSA-DBN [4] 3 95 STFT+CNN [32] 3 96 WPT-MWSVD+SVM [5] 3 87.8 CNN-BLSTM [30] 3 99.2 ResNet-STAC-tanh [31] 3 90.77 RCMFE+SOF [6] 3 95.8…”

Section: Methods Fault Types Accuracy (%)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Time-Frequency Compression Fusion and Residual Time-Frequency Mixed Attention Network

et al. 2022

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“…However, due to the complexity of vibration signals, single-scale entropy cannot fully reflect fault information, thus multiscale signals can fully excavate fault information. Subsequently, the methods of time series multiscale [32], composite multiscale [33], refine composite multiscale [34] are gradually put forward by scholars. Among them, refined composite multiscale dispersion entropy (RCMDE) has better stability and feature extraction ability than multiscale dispersion entropy (MDE) [35].…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Yang²,

et al. 2022

Sensors

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Rolling bearings are the vital components of large electromechanical equipment, thus it is of great significance to develop intelligent fault diagnoses for them to improve equipment operation reliability. In this paper, a fault diagnosis method based on refined composite multiscale reverse dispersion entropy (RCMRDE) and random forest is developed. Firstly, rolling bearing vibration signals are adaptively decomposed by variational mode decomposition (VMD), and then the RCMRDE values of 25 scales are calculated for original signal and each decomposed component as the initial feature set. Secondly, based on the joint mutual information maximization (JMIM) algorithm, the top 15 sensitive features are selected as a new feature set and feed into random forest model to identify bearing health status. Finally, to verify the effectiveness and superiority of the presented method, actual data acquisition and analysis are performed on the bearing fault diagnosis experimental platform. These results indicate that the presented method can precisely diagnose bearing fault types and damage degree, and the average identification accuracy rate is 97.33%. Compared with the refine composite multiscale dispersion entropy (RCMDE) and multiscale dispersion entropy (MDE), the fault diagnosis accuracy is improved by 2.67% and 8.67%, respectively. Furthermore, compared with the RCMRDE method without VMD decomposition, the fault diagnosis accuracy is improved by 3.67%. Research results prove that a better feature extraction technique is proposed, which can effectively overcome the deficiency of existing entropy and significantly enhance the ability of fault identification.

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“…A Euclidean distance based multi-scale fuzzy entropy method has been proposed to diagnose bearing faults, which measures the similarity of two vectors with continuous values from zero to one based on the Euclidean distance of the two vectors [ 13 ]. An improved FE named refined composite multi-scale fuzzy entropy (RCMFE) has been applied to diagnose the significant bearing fault [ 14 ]. Being different from AE, SE, RCMFE, and FE, PE compares and analyzes the order of amplitude values to obtain the corresponding feature information rather than considering the value of the time series.…”

Section: Introductionmentioning

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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Cross‐domain bearing fault diagnosis with refined composite multiscale fuzzy entropy and the self organizing fuzzy classifier

Cited by 7 publications

References 32 publications

Rolling Bearing Fault Diagnosis Based on Time-Frequency Compression Fusion and Residual Time-Frequency Mixed Attention Network

Rolling Bearing Fault Diagnosis Based on Time-Frequency Compression Fusion and Residual Time-Frequency Mixed Attention Network

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

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

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