Improved Intrinsic Time-Scale Decomposition Method and its Simulation

Lin, Jin

doi:10.4028/www.scientific.net/amm.121-126.2045

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…To solve this drawback, many schemes have been proposed. 17,18 Hu et al proposed a new approach named ensemble intrinsic time-scale decomposition (EITD) to deal with end effects and avoid distortion. 19 The steps included using cubic spline interpolation to fit baseline control points and mirror extension.…”

Section: Introductionmentioning

confidence: 99%

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Rolling bearing fault diagnosis based on adaptive smooth ITD and MF-DFA method

Yuan

Peng

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

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To effectively utilize a feature set to further improve fault diagnosis of a rolling bearing vibration signal, a method based on multi-fractal detrended fluctuation analysis (MF-DFA) and smooth intrinsic timescale decomposition (SITD) was proposed. The vibration signal was decomposed into several proper rotation components by applying this new SITD method to overcome noise effects, preserve the effective signal, and improve the signal-to-noise ratio. Wavelet analysis was embedded in iteration procedures of intrinsic timescale decomposition (ITD). For better results, an adaptive threshold function was used for signal recovery from noisy proper rotation components in the wavelet domain. Additionally, MF-DFA was used to reveal the multi-fractality present in the instantaneous amplitude of the proper rotation components. Finally, linear local tangent space alignment was applied for feature dimension reduction and to obtain fault characteristics of different types, further improving identification accuracy. The performance of the proposed method is determined to be superior to that of the ITD-MF-DFA method.

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Section: Introductionmentioning

confidence: 99%

“…To solve this drawback, many schemes have been proposed. 17,18 Hu et al. proposed a new approach named ensemble intrinsic time-scale decomposition (EITD) to deal with end effects and avoid distortion.…”

Section: Introductionmentioning

confidence: 99%

Rolling bearing fault diagnosis based on adaptive smooth ITD and MF-DFA method

Yuan

Peng

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

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An improved intrinsic time-scale decomposition method based on adaptive noise and its application in bearing fault feature extraction

Zhan

et al. 2020

Meas. Sci. Technol.

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This study proposes a new method for detecting and diagnosing fault information for rolling bearings. First, a novel method called complete ensemble intrinsic timescale decomposition with adaptive noise is proposed. Compared with the complete ensemble empirical modes decomposition with adaptive noise (CEEMDAN) method, the proposed method reduces the spurious mode, which interferes with the identification of feature frequency in rolling bearings. In addition, when a typical mode mixing signal is decomposed, the mode mixing problem that appears in the adjacent mode components of the CEEMDAN method is also solved. Second, there is only an intrinsic mode function, which mostly reflects the vibration feature of a rolling bearing. We propose a new method to evaluate these mode functions to determine which mode function contains the most useful information. Our proposed method solves the problem using traditional time-domain analysis methods of a single dimensionless parameter with poor stability and low sensitivity. Our method includes sample entropy, kurtosis, the zero-crossing rate, and a correlation coefficient. The proposed method reduces the limitations of each method to select the sensitive component. Finally, we use a simulation signal and an experimental signal to verify the effectiveness of the proposed method.

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Application of complete ensemble intrinsic time-scale decomposition and least-square SVM optimized using hybrid DE and PSO to fault diagnosis of diesel engines

Zhang

Liu

2017

Frontiers Inf Technol Electronic Eng

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Improved Intrinsic Time-Scale Decomposition Method and its Simulation

Cited by 3 publications

References 5 publications

Rolling bearing fault diagnosis based on adaptive smooth ITD and MF-DFA method

Rolling bearing fault diagnosis based on adaptive smooth ITD and MF-DFA method

An improved intrinsic time-scale decomposition method based on adaptive noise and its application in bearing fault feature extraction

Application of complete ensemble intrinsic time-scale decomposition and least-square SVM optimized using hybrid DE and PSO to fault diagnosis of diesel engines

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