Oscillation based permutation entropy calculation as a dynamic nonlinear feature for health monitoring of rolling element bearing

Noman, Khandaker; Wang, Dong; Peng, Zhike; He, Qingbo

doi:10.1016/j.measurement.2020.108891

Cited by 31 publications

(12 citation statements)

References 43 publications

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“…However, the energy moment entropy [22] proposed by Gao cannot detect the fault warning point until the 538th time point. Similarly, the oscillation based permutation entropy [13] proposed by Noman cannot detect the fault warning point until the 533th time point. The results show that the SDE proposed in this paper can detect the fault warning point of mechanical parts earlier and provide more response time for the repair and replacement of parts.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…Kumar extracted the Shannon entropy, permutation entropy (PE) and approximate entropy (AE) degradation features of the bearing and then constructed a bearing degradation trend model by gaussian process regression [12]. Noman first separated the oscillating eigenvalues from the vibration signal, then took the PE of the oscillation signal as the bearing degradation feature [13]. Minhas obtained several modes by empirical mode decomposition, then extracted the weighted multi-scale entropy degradation features of sensitive modes by Hurst index [14].…”

Section: Introductionmentioning

confidence: 99%

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Sliding Dispersion Entropy-Based Fault State Detection for Diaphragm Pump Parts

et al. 2021

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Aiming at the disadvantages of low trend, poor characterization performance, and poor anti-noise performance of traditional degradation features such as dispersion entropy (DE), a fault detection method based on sliding dispersion entropy (SDE) is proposed. Firstly, a sliding window is added to the signal before extracting the DE feature, and the root mean square of the signal inside the sliding window is used to replace the signal in the window to realize down sampling, which enhances the trend of DE. Secondly, the hyperbolic tangent sigmoid function (TANSIG) is introduced to map the signals to different categories when extracting the DE feature, which is more in line with the signal distribution of mechanical parts and the monotonicity of the degradation feature is improved. For noisy signal, the introduction of locally weighted scatterplot smoothing (LOWESS) can remove the burrs and fluctuations of the SDE curve, and the anti-noise performance of SDE is improved. Finally, the SDE state warning line is constructed based on the 2σ criterion, which can determine the fault warning point in time and effectively. The state detection results of bearing and check valve show that the proposed SDE improves the trend, monotonicity, and robustness of the state tracking curve, and provides a new method for fault state detection of mechanical parts.

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Section: Comparison and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sliding Dispersion Entropy-Based Fault State Detection for Diaphragm Pump Parts

et al. 2021

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“…Recently, using this method to realize rolling bearing fault diagnosis has become a research hotspot [9][10][11]. Obtained low oscillatory component is analogous to the fault impulses of rolling bearings due to their exponentially decaying character [12].…”

Section: Introductionmentioning

confidence: 99%

Application of oscillatory time frequency manifold for extraction of rolling element bearing fault signature

Noman

et al. 2022

J. Phys.: Conf. Ser.

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To overcome the problem that traditional feature extraction algorithms are sensitive to noise, a bearing fault signature extraction scheme is proposed in this paper with the help of oscillation-based signal decomposition and time frequency manifold (TFM) learning. Firstly, an oscillation-based signal component separation method based on tunable Q factor wavelet transform (TQWT) is utilized to separate the low oscillatory component from vibration signals. Then, concept of TFM is utilized on the separated low oscillatory component to generate the low oscillatory time frequency manifold signature. The proposed method is termed as oscillatory time frequency manifold (OTFM). Compared to that of traditional short time Fourier transform (STFT) and original TFM algorithm, results of experiment show that the proposed algorithm has better time frequency characterization ability for bearing fault signature.

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“…The vibration-signal data obtained by VMD decomposition needs further processing to extract effective fault-feature information. A series of feature-extraction methods based on entropy are widely used in the field of fault diagnosis, such as sample entropy (SE) [ 11 ], permutation entropy (PE) [ 12 ], and fuzzy entropy (FE) [ 13 ]. However, the above methods are based on single-scale analysis of time series and cannot reflect complex features.…”

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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Oscillation based permutation entropy calculation as a dynamic nonlinear feature for health monitoring of rolling element bearing

Cited by 31 publications

References 43 publications

Sliding Dispersion Entropy-Based Fault State Detection for Diaphragm Pump Parts

Sliding Dispersion Entropy-Based Fault State Detection for Diaphragm Pump Parts

Application of oscillatory time frequency manifold for extraction of rolling element bearing fault signature

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

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