A scale independent flexible bearing health monitoring index based on time frequency manifold energy &amp; entropy

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

doi:10.1088/1361-6501/ab9412

Cited by 31 publications

(14 citation statements)

References 41 publications

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“…In recent years, entropy theory [24] has been increasingly used to characterize nonlinear signals, particularly in the analysis of rolling bearing faults and the measurement of vibration signal complexity. Common methods include approximate entropy [25], sample entropy (SE) [26], PE [27], symbolic entropy (SE) [28], fuzzy entropy (FE) [29], and scattering entropy (SE) [30].…”

Section: Introductionmentioning

confidence: 99%

Study on the optimization of the classification method of rolling bearing fault type and damage degree based on SFO–VMD

Jing,

Bian,

et al. 2023

Meas. Sci. Technol.

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Currently, rolling bearings operate in harsh environments, resulting in acquired signals with a low signal-to-noise ratio. In light of this, this paper proposes an improved variational modal decomposition(VMD) combined with refine composite multi-scale fuzzy entropy (RCMFE) and linear support vector machine (LSVM) for fault diagnosis. Firstly, the sailfish optimization (SFO) algorithm is employed to optimize the important parameter combinations in the VMD algorithm, using the envelope entropy as its objective function. The analysis includes both simulated and real measured signals with varying signal-to-noise ratios. The results demonstrate that, compared to traditional manual parameter setting and empirical modal decomposition methods, this approach effectively addresses the parameter setting issue of VMD in the signal decomposition process. Additionally, it successfully eliminates noise to extract the fault characteristic signal hidden within the original signal. Secondly, the RCMFE algorithm is introduced to overcome the problem of commonly used dimensioned and dimensionless indicators being influenced by load and speed when used as characteristic indicators. By analyzing the influence of load and speed on the RCMFE value, the results demonstrate its strong stability as a feature indicator, unaffected by these factors. For the intelligent classification of failure type and damage degree, LSVM is chosen as the classification method. Analysis results indicate that the distribution characteristics of RCMFE values align better with LSVM compared to the common radial basis function support vector machine, resulting in a significant improvement in diagnosis accuracy.

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Section: Introductionmentioning

confidence: 99%

Study on the optimization of the classification method of rolling bearing fault type and damage degree based on SFO–VMD

Jing,

Bian,

et al. 2023

Meas. Sci. Technol.

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“…As a result, they are not suitable for application in continuous monitoring of REB health. 22,23 Furthermore, as an alternative signal preprocessing technique, adaptive decomposition methods such as empirical mode decomposition or local mean decomposition suffer from mode mixing problem. 24,25 Different from aforementioned signal preprocessing techniques, lately square envelope (SE) analysis has been proven effective for extracting the REB fault features.…”

Section: Introductionmentioning

confidence: 99%

Continuous monitoring of rolling element bearing health by nonlinear weighted squared envelope-based fuzzy entropy

Noman

Wen

et al. 2023

Structural Health Monitoring

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Fuzzy entropy (FE) can be regarded as an effective measure for nonlinear characterization of rolling element bearing (REB) health condition by quantifying the complexity of vibration signals. However, during continuous monitoring operation under heavy noise, transient impulses corresponding to a REB fault get submerged under unnecessary random noise components. As a consequence, FE algorithm not only fails to detect a REB fault at the earliest point of inception but also performs poorly in monitoring the development of the incepted fault in an efficient manner. Aiming at solving the aforementioned limitations of FE in continuous monitoring of REB health, background noise associated with collected vibration signals is eliminated by weighting the corresponding square envelope signal. Due to the utilization of weighted squared envelope signal, the proposed measure is termed as weighted square envelope-based FE (WSEFE). One simulated case and two different run-to-failure experimental cases are used for validation. The comparison results demonstrate that the proposed WSEFE not only overcomes the limitations of original FE but also performs better than conventional permutation entropy and advanced FE-based measure multiscale FE (MFE) in continuous monitoring of REB health.

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“…Tang et al [22] used the LLTSA algorithm to compress the highdimensional vectors of the samples so that the samples have better resolution. In recent years, many scholars began to combine time-frequency analysis with manifold learning, which not only analyzed the time-frequency manifold structure of signals but also achieved a good effect of noise suppression [23][24][25]. He et al [26] analyzed the nonlinear timefrequency manifold structure of defective signals, and the extracted signal features were suitable for the diagnosis of mechanical faults.…”

Section: Introductionmentioning

confidence: 99%

A New De-Noising Method Based on Enhanced Time-Frequency Manifold and Kurtosis-Wavelet Dictionary for Rolling Bearing Fault Vibration Signal

Tong

Liu

et al. 2022

Sensors

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The transient pulses caused by local faults of rolling bearings are an important measurement information for fault diagnosis. However, extracting transient pulses from complex nonstationary vibration signals with a large amount of background noise is challenging, especially in the early stage. To improve the anti-noise ability and detect incipient faults, a novel signal de-noising method based on enhanced time-frequency manifold (ETFM) and kurtosis-wavelet dictionary is proposed. First, to mine the high-dimensional features, the C-C method and Cao’s method are combined to determine the embedding dimension and delay time of phase space reconstruction. Second, the input parameters of the liner local tangent space arrangement (LLTSA) algorithm are determined by the grid search method based on Renyi entropy, and the dimension is reduced by manifold learning to obtain the ETFM with the highest time-frequency aggregation. Finally, a kurtosis-wavelet dictionary is constructed for selecting the best atom and eliminating the noise and reconstruct the defective signal. Actual simulations showed that the proposed method is more effective in noise suppression than traditional algorithms and that it can accurately reproduce the amplitude and phase information of the raw signal.

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A scale independent flexible bearing health monitoring index based on time frequency manifold energy & entropy

Cited by 31 publications

References 41 publications

Study on the optimization of the classification method of rolling bearing fault type and damage degree based on SFO–VMD

Study on the optimization of the classification method of rolling bearing fault type and damage degree based on SFO–VMD

Continuous monitoring of rolling element bearing health by nonlinear weighted squared envelope-based fuzzy entropy

A New De-Noising Method Based on Enhanced Time-Frequency Manifold and Kurtosis-Wavelet Dictionary for Rolling Bearing Fault Vibration Signal

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