A fault pattern recognition method for rolling bearing based on CELMDAN and fuzzy entropy

Liu, Ning; Liu, Bing; Jiaxin, Wei; Xi, Cungen

doi:10.21595/jve.2020.21282

Cited by 3 publications

(5 citation statements)

References 17 publications

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“…Because the vibration of the human chest is periodic, the slow time direction data changes relatively regularly, and the permutation entropy value should be small, so it can be used for the positioning of human targets. When the three volunteers were 3 m, 6 m and 9 m away from the radar device, the results of calculating normalized permutation entropy, sample entropy [28] and fuzzy entropy [29] of radar echo data are shown in Figure 2. When the radar echo signal contains human vital signs, the three entropy values decrease significantly.…”

Section: Target Recognition and Locationmentioning

confidence: 99%

“…relatively regularly, and the permutation entropy value should be small, so it can be used for the positioning of human targets. When the three volunteers were 3 m, 6 m and 9 m away from the radar device, the results of calculating normalized permutation entropy, sample entropy [28] and fuzzy entropy [29] of radar echo data are shown in Figure 2.…”

Section: Target Recognition and Locationmentioning

confidence: 99%

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A Multi-Target Localization and Vital Sign Detection Method Using Ultra-Wide Band Radar

Zhang

Cheng

et al. 2023

Sensors

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Life detection technology using ultra-wideband (UWB) radar is a non-contact, active detection technology, which can be used to search for survivors in disaster rescues. The existing multi-target detection method based on UWB radar echo signals has low accuracy and has difficulty extracting breathing and heartbeat information at the same time. Therefore, this paper proposes a new multi-target localization and vital sign detection method using ultra-wide band radar. A target recognition and localization method based on permutation entropy (PE) and K means++ clustering is proposed to determine the number and position of targets in the environment. An adaptive denoising method for vital sign extraction based on ensemble empirical mode decomposition (EEMD) and wavelet analysis (WA) is proposed to reconstruct the breathing and heartbeat signals of human targets. A heartbeat frequency extraction method based on particle swarm optimization (PSO) and stochastic resonance (SR) is proposed to detect the heartbeat frequency of human targets. Experimental results show that the PE—K means++ method can successfully recognize and locate multiple human targets in the environment, and its average relative error is 1.83%. Using the EEMD–WA method can effectively filter the clutter signal, and the average relative error of the reconstructed respiratory signal frequency is 4.27%. The average relative error of heartbeat frequency detected by the PSO–SR method was 6.23%. The multi-target localization and vital sign detection method proposed in this paper can effectively recognize all human targets in the multi-target scene and provide their accurate location and vital signs information. This provides a theoretical basis for the technical system of emergency rescue and technical support for post-disaster rescue.

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Section: Target Recognition and Locationmentioning

confidence: 99%

Section: Target Recognition and Locationmentioning

confidence: 99%

A Multi-Target Localization and Vital Sign Detection Method Using Ultra-Wide Band Radar

Zhang

Cheng

et al. 2023

Sensors

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“…As wavelet transform (Shruti et al, 2020) and PCA (Li et al, 2018) algorithm already has mature applications, details can be referred to references.…”

Section: Theorymentioning

confidence: 99%

“…In the study on failure characteristic extraction based on signal decomposition algorithms, the option of component signals that are sensitive to failures is of great significance (Li et al, 2018; Wang et al, 2018). A bearing failure is often accompanied by impulse signals.…”

Section: Introductionmentioning

confidence: 99%

“…When data dimension are large enough and each data can provide abundant information, we need to reduce the dimensions and make blending to treat data sets (Mu et al, 2019). Principal component analysis (PCA) is a common data blending algorithm (Li et al, 2018). In considering that cumulative variance contribution rate is usually taken to measure the performance of information storage included in principal elements, cumulative variance contribution rate can directly reflect the precision of principle element model (Li et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

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A novel self-adaptive option method for sensitive failure component signals and its application in rolling bearings

Fang

Guo

et al. 2023

Journal of Vibration and Control

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Bearing is the most vulnerable key part in rotating machine and bears important influence on the safety of equipment. Weakness and complexity are the two features of fault characteristic information carried by signals in the early stage of fault. For that, a fault is difficult to be recognized correctly. To identify a compound failure of bearing, the paper has brought forward a new self-adaptive option method for component signals that are sensitive to failure feature information of bearing. The sensitivity of kurtosis to bearing failure is exploited and the influence of signal complexity on the extraction of failure feature information is taken seriously, the paper has proposed the self-adaptive option method for component signals that are sensitive to failure feature by combined kurtosis with Complexity parameter included in Hjorth parameters. Furthermore, as the mid-value represents the general level of signal and is not affected by larger or smaller data, with the mid-values of kurtosis and Complexity parameter as the boundary, the paper has chosen the component signals which can more comprehensively show the failure features of bearing. Additionally, by principal component analysis (PCA), component signals selected are blended and reconstructed. Finally, by the Hilbert envelope spectrum of signals reconstructed, failure types of bearing are identified. To verify the effectiveness of presented method, the presented method is compared with conventional method on the basis of the exactly consistent data. The result indicates that the proposed method is superior to the traditional one in extracting fault information and identifying the multiple failure types of bearing.

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The Effect of Data Decomposition on Prediction Performance in Wind Speed Prediction with Artificial Neural Network

ŞENKAL,

EMEKSİZ

2023

International Scientific and Vocational Studies Journal

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This study investigates the effect of data decomposition to improve the performance of artificial neural networks (ANNs), widely used in wind speed forecasting in the wind energy sector. Artificial neural networks are essential tools for planning and optimizing the daily generation of wind power plants. However, prediction errors can lead to significant problems in power generation and energy grid management. The results show that data decomposition substantially affects the wind speed forecasting performance of neural networks. These findings are essential for researchers and industry professionals interested in developing more accurate forecasting models for power generation planning and management in the wind energy sector. By integrating artificial neural networks and data disaggregation methods, the study stands out as an essential step forward to improve the accuracy of wind speed forecasts and optimize the efficiency of wind energy facilities.

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A fault pattern recognition method for rolling bearing based on CELMDAN and fuzzy entropy

Cited by 3 publications

References 17 publications

A Multi-Target Localization and Vital Sign Detection Method Using Ultra-Wide Band Radar

A Multi-Target Localization and Vital Sign Detection Method Using Ultra-Wide Band Radar

A novel self-adaptive option method for sensitive failure component signals and its application in rolling bearings

The Effect of Data Decomposition on Prediction Performance in Wind Speed Prediction with Artificial Neural Network

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