A novel Fast Entrogram and its applications in rolling bearing fault diagnosis

Zhang, Kun; Xu, Yonggang; Liao, Zhiqiang; Song, Liuyang; Peng, Chen

doi:10.1016/j.ymssp.2020.107582

Cited by 105 publications

(37 citation statements)

References 32 publications

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“…The empirical wavelet function and empirical scale function are given by Eqs. ( 1) and (2), and the values of β and γ by Eqs. (3) and (4).…”

Section: Ewt Theorymentioning

confidence: 99%

“…Reliable identification of abnormal data and effective differentiation of abnormal patterns are important foundations for achieving on-line monitoring data cleaning and an accurate understanding of equipment operating status. In the anomaly data identification process, time series decomposition [1], time series transformation [2], statistics based and clustering based methods are mainly used. Reference [3] constructed a robust covariance estimator through the idea of iteration and Mahalanobis distance and uses this as a basis to calculate robust correlation parameters, thereby achieving effective detection of transformer oil chromatography outliers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Anomaly Detection and Pattern Differentiation in Monitoring Data from Power Transformers

Zhao¹,

Gao²,

Liu³

et al. 2022

Energy Engineering

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Aiming at the problem of abnormal data generated by a power transformer on-line monitoring system due to the influences of transformer operation state change, external environmental interference, communication interruption, and other factors, a method of anomaly recognition and differentiation for monitoring data was proposed. Firstly, the empirical wavelet transform (EWT) and the autoregressive integrated moving average (ARIMA) model were used for time series modelling of monitoring data to obtain the residual sequence reflecting the anomaly monitoring data value, and then the isolation forest algorithm was used to identify the abnormal information, and the monitoring sequence was segmented according to the recognition results. Secondly, the segmented sequence was symbolised by the improved multi-dimensional SAX vector representation method, and the assessment of the anomaly pattern was made by calculating the similarity score of the adjacent symbol vectors, and the monitoring sequence correlation was further used to verify the assessment. Finally, the case study result shows that the proposed method can reliably recognise abnormal data and accurately distinguish between invalid and valid anomaly patterns.

show abstract

“…The empirical wavelet function and empirical scale function are given by Eqs. ( 1) and (2), and the values of β and γ by Eqs. (3) and (4).…”

Section: Ewt Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Anomaly Detection and Pattern Differentiation in Monitoring Data from Power Transformers

Zhao¹,

Gao²,

Liu³

et al. 2022

Energy Engineering

View full text Add to dashboard Cite

show abstract

“…Therefore, this paper proposes a new bearing-fault diagnosis method, which integrates several algorithms to help quickly detect bearing faults and reduce losses. This paper obtains fault information from the vibrational signals of rolling bearings [ 3 , 4 ]: feature extraction and fault classification.…”

Section: Introductionmentioning

confidence: 99%

Coordinated Approach Fusing RCMDE and Sparrow Search Algorithm-Based SVM for Fault Diagnosis of Rolling Bearings

Wang

Zhang

2021

Sensors

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We propose a novel fault-diagnosis approach for rolling bearings by integrating variational mode decomposition (VMD), refined composite multiscale dispersion entropy (RCMDE), and support vector machine (SVM) optimized by a sparrow search algorithm (SSA). Firstly, VMD was selected from various signal decomposition methods to decompose the original signal. Then, the signal features were extracted by RCMDE as the input of the diagnosis model. Compared with multiscale sample entropy (MSE) and multiscale dispersion entropy (MDE), RCMDE proved to be superior. Afterwards, SSA was used to search the optimal parameters of SVM to identify different faults. Finally, the proposed coordinated VMD–RCMDE–SSA–SVM approach was verified and evaluated by the experimental data collected by the wind turbine drivetrain diagnostics simulator (WTDS). The results of the experiments demonstrate that the proposed approach not only identifies bearing fault types quickly and effectively but also achieves better performance than other comparative methods.

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“…At present, many scholars use different methods to study the vibration signal of fault bearing [5][6][7][8][9][10][11]. In the field of fault diagnosis, some scholars study and apply VMD.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearing Based on Improved VMD and KNN

Shen

Wang

et al. 2021

Mathematical Problems in Engineering

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Variational modal decomposition (VMD) has the end effect, which makes it difficult to efficiently obtain fault eigenvalues from rolling bearing fault signals. Inspired by the mirror extension, an improved VMD is proposed. This method combines VMD and mirror extension. The mirror extension is a basic algorithm to inhibit the end effect. A comparison is made with empirical mode decomposition (EMD) for fault diagnosis. Experiments show that the improved VMD outperforms EMD in extracting the fault eigenvalues. The performance of the new algorithm is proven to be effective in real-life mechanical fault diagnosis. Furthermore, in this article, combining with singular value decomposition (SVD), fault eigenvalues are extracted. In this way, fault classification is realized by K-nearest neighbor (KNN). Compared with EMD, the proposed approach has advantages in the recognition rate, which can accurately identify fault types.

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A novel Fast Entrogram and its applications in rolling bearing fault diagnosis

Cited by 105 publications

References 32 publications

Anomaly Detection and Pattern Differentiation in Monitoring Data from Power Transformers

Anomaly Detection and Pattern Differentiation in Monitoring Data from Power Transformers

Coordinated Approach Fusing RCMDE and Sparrow Search Algorithm-Based SVM for Fault Diagnosis of Rolling Bearings

Fault Diagnosis of Rolling Bearing Based on Improved VMD and KNN

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