Dense multi-scale entropy and it’s application in mechanical fault diagnosis

Zhao, Dongfang; Liu, Shulin; Cheng, Shouguo; Sun, Xin; Wang, Lu; Yuan, Wancheng; Zhang, Hongli

doi:10.1088/1361-6501/aba4da

Cited by 9 publications

(10 citation statements)

References 41 publications

(46 reference statements)

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“…The frequency domain analysis can reveal more information than the amplitude domain parameters, but the analysis ability of this kind of non-stationary nonlinear signal is limited due to the constraints of Fourier global transform. In recent years, nonlinear analysis methods have gradually attracted people's attention because they can ively extract the nonlinear features hidden in vibration signals [66][67][68]. Nonlinear dynamic parameters can effectively evaluate the energy distribution, degree of chaos and other information of vibration signal, which can better reveal the deep information of vibrational state.…”

Section: Nonlinear Dynamic Parameter Analysismentioning

confidence: 99%

Intelligent chatter detection for CNC machine based on RFE multi-feature selection strategy

Wang¹,

Yuan²,

Liu³

et al. 2021

Meas. Sci. Technol.

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Chatter is an unstable self-excited vibration generated during processing. It not only reduces the machining efficiency, machining accuracy, the service life of machine tools and cutting tools, but also results in sound pollution and material waste. To improve the machining stability and product quality of thin-walled workpieces, effective chatter detection of machine tools is essential. This paper presented a signal feature evaluation model and multi-feature recognition system for chatter detection. First, the original signals obtained from the acceleration sensor were processed through local mean decomposition to reduce the noise in the signal. Thereafter, the correlation between the system state and the different features of amplitude domain, frequency domain and nonlinear domain was analyzed. Further, through the feature evaluation model based on recursive feature elimination, the main feature parameters related to machine tool state are obtained, and different recognition algorithms were used to verify the rationality of the fusion features. Finally, an end-to-end chatter detection method and the corresponding software system have been established. Experimental results show that the proposed method can effectively improve the accuracy of vibration detection.

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Section: Nonlinear Dynamic Parameter Analysismentioning

confidence: 99%

Intelligent chatter detection for CNC machine based on RFE multi-feature selection strategy

Wang¹,

Yuan²,

Liu³

et al. 2021

Meas. Sci. Technol.

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“…To overcome these limitations with MSE methods, another method is proposed which subdivides scale factors. This new method is termed as dense MSE (DMSE) 35 . For DMSE, the original raw sequence is expanded guaranteeing the characteristics of the original time series remain unchanged in the expanded series.…”

Section: Introductionmentioning

confidence: 99%

Eigen-Entropy based Time Series Signatures to Support Multivariate Time Series Classification

Patharkar,

Huang,

et al. 2024

Preprint

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Most current algorithms for multivariate time series classification tend to overlook the correlations between time series of different variables. In this research, we propose a framework that leverages Eigen-entropy along with a cumulative moving window to derive time series signatures to support the classification task. These signatures are enumerations of correlations among different time series considering the temporal nature of the dataset. To manage dataset's dynamic nature, we employ preprocessing with dense multi scale entropy. Consequently, the proposed framework, Eigen-entropy-based Time Series Signatures, captures correlations among multivariate time series without losing its temporal and dynamic aspects. The efficacy of our algorithm is assessed using six binary datasets sourced from the University of East Anglia (UEA), in addition to a publicly available sepsis dataset and an institutional sepsis dataset from the Mayo Clinic. We employ recall as a metric for evaluation against the baseline algorithm, Dependent Dynamic Time Warping with 1 Nearest Neighbor. Our algorithm outperforms the baseline algorithm for UEA datasets in terms of recall with an average value of 10% across all datasets, and by 38% for an institutional sepsis dataset. For the public sepsis dataset, the performance of our algorithm is comparable to the baseline algorithm.

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“…by bearing failure are huge every year, so intelligent health maintenance based on bearing performance degradation monitoring and remaining life prediction has become a hot topic for researchers. Intelligent health maintenance of bearings usually includes two stages: first, it is necessary to build a health evaluation index that can track the bearing degradation process, which can reflect the health of the bearing at different times [1][2][3]. Then establish a prediction model and predict the remaining useful life of the bearing based on the health evaluation index [4,5].…”

Section: Introductionmentioning

confidence: 99%

Bearing performance degradation assessment and remaining useful life prediction based on data-driven and physical model

Sheng¹,

Liu²,

Li³

2023

Meas. Sci. Technol.

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Intelligent health maintenance of bearings usually consists of two stages: constructing effective health assessment indicators and accurate remaining useful life prediction models. However, many prediction models are available only when many constraints are met, and the health assessment indicators may not be able to accurately track the performance degradation process of bearings. This study proposes a bearing performance degradation assessment and remaining life prediction method. First, the health evaluation index family (referred to as the generalized high-order moment coefficient) was constructed based on the generalized power mean and high-order origin moments for health assessment. Subsequently, an improved Paris–Erdogan model is proposed, which uses the optimal health evaluation index as input to predict the remaining life of the bearing after the initial failure. The experimental results show that the proposed method has a higher performance degradation tracking accuracy and a smaller prediction error than the combination of traditional statistical indicators and prediction models.

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Dense multi-scale entropy and it’s application in mechanical fault diagnosis

Cited by 9 publications

References 41 publications

Intelligent chatter detection for CNC machine based on RFE multi-feature selection strategy

Intelligent chatter detection for CNC machine based on RFE multi-feature selection strategy

Eigen-Entropy based Time Series Signatures to Support Multivariate Time Series Classification

Bearing performance degradation assessment and remaining useful life prediction based on data-driven and physical model

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