Bi-filter multiscale-diversity-entropy-based weak feature extraction for a rotor-bearing system

Li, Yongbo; Wang, Xinyue; Zheng, Jianhua; Feng, Ke; Ji, Jinchen

doi:10.1088/1361-6501/acbd66

Cited by 5 publications

(3 citation statements)

References 34 publications

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“…of whole mechanical system [1]. Thus, the practical and accurate prognostic and health management (PHM) of rolling bearings can grasp the health status in real-time for fault detection and remaining useful life (RUL) prediction, and it has received great attention nowadays [2][3][4]. In the study of PHM, RUL means the normal service life of the machinery before the occurrence of the failures [5].…”

Section: Introductionmentioning

confidence: 99%

A novel cross-domain adaption network based on Se-Sk-DenseNet for remaining useful life prediction of rolling bearings under different working conditions

Guo,

Li,

Dong

et al. 2024

Meas. Sci. Technol.

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Effectively predicting the remaining using life (RUL) of the rolling bearings can ensure the reliability and safety, which can make full use of their life, minimize machine downtime, and reduce the operation and maintenance cost of enterprises. To solve the problems of data distribution discrepancy caused by different working conditions and the collected signals containing a lot of useless information and noise, a novel cross-domain adaption network (CDAN) is proposed in this study. Firstly, a novel feature extractor, Se-Sk-DenseNet, is developed to extract useful critical features from the input data and remove the ineffective features by embedding Se and Sk attention blocks; Besides, a new objective loss function consist of the RUL loss, the MK-MMD loss, the contrastive loss, and the KL divergence loss, is proposed to solve the problem of data distribution shift; Finally, the effectiveness and superiority of CDAN are proved on the PHM2012 bearings dataset. The results demonstrate that CDAN can extract deep critical features and achieve the high cross-domain RUL prediction accuracy under different working conditions.

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Section: Introductionmentioning

confidence: 99%

A novel cross-domain adaption network based on Se-Sk-DenseNet for remaining useful life prediction of rolling bearings under different working conditions

Guo,

Li,

Dong

et al. 2024

Meas. Sci. Technol.

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“…Kumar et al [10] presented a method using the Minimum Entropy Deconvolution (MED) technique and zero-frequency filter to identify the incipient motor bearing faults. Li et al [11] proposed a bi-filter multi-scale diversity entropy method (BMDE) for effectively identifying fault information in a rotor-bearing system. Hao et al [12] proposed an enhanced filtering and feature enhancement approach for the bearing diagnosis of spindle motors.…”

Section: Introductionmentioning

confidence: 99%

Application of an Improved Laplacian-of-Gaussian Filter for Bearing Fault Signal Enhancement of Motors

Tang,

Xu,

Liu

2024

Machines

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The presence of strong noise and vibration interference in fault vibration signals poses challenges for extracting fault features from motor bearings. Therefore, appropriate pre-filtering procedures can effectively suppress the impact of the noise interference and further enhance fault-related signals. In this work, an improved Laplacian-of-Gaussian (ILoG) filter is proposed to enhance the fault-related signal. The proposed ILoG approach employs an enhanced Kurtosis-based indicator known as Correlated Kurtosis (CK). The CK capitalizes on the cyclostationarity of fault-related impulses and mitigates the random nature of impulse noise. Subsequently, an objective function, based on CK statistics, is suggested to iteratively update LoG coefficients by maximizing the CK value of the output signal. Therefore, the ILoG filter can better highlight the fault cyclic impulses associated with bearing faults. Furthermore, the ILoG filter is capable of attenuating impulsive noise, a feature that is absent in the original LoG filter. The simulation and experimental results demonstrate that the proposed ILoG method provides a remarkable capability to effectively enhance the fault-induced components, thereby improving the diagnostic accuracy. Consequently, the ILoG filter holds great potential for application in motor bearing fault diagnosis.

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“…Hierarchical entropy [23] is considered as an entropy method that takes into account the high-frequency information of a sequence and essentially coarse-grains the sequence using two operators-difference and average. However, hierarchical entropy is unable to consider multiscale information concurrently, and the coarse-grained sequences in different layers do not distribute as highfrequency components or low-frequency components [24]. In addition, after the multiscale entropy feature extraction of the sequences, the feature selection was not considered in most cases, which could create the problem of redundancy of feature information.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Rolling Bearings Based on a Complete Frequency Range Feature Extraction and Combined Feature Selection Methodology

Xue,

Huang,

Zhang

et al. 2023

Sensors

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The utilization of multiscale entropy methods to characterize vibration signals has proven to be promising in intelligent diagnosis of mechanical equipment. However, in the current multiscale entropy methods, only the information in the low-frequency range is utilized and the information in the high-frequency range is discarded. In order to take full advantage of the information, in this paper, a fault feature extraction method utilizing the bidirectional composite coarse-graining process with fuzzy dispersion entropy is proposed. To avoid the redundancy of the full frequency range feature information, the Random Forest algorithm combined with the Maximum Relevance Minimum Redundancy algorithm is applied to feature selection. Together with the K-nearest neighbor classifier, a rolling bearing intelligent diagnosis framework is constructed. The effectiveness of the proposed framework is evaluated by a numerical simulation and two experimental examples. The validation results demonstrate that the extracted features by the proposed method are highly sensitive to the bearing health conditions compared with hierarchical fuzzy dispersion entropy, composite multiscale fuzzy dispersion entropy, multiscale fuzzy dispersion entropy, multiscale dispersion entropy, multiscale permutation entropy, and multiscale sample entropy. In addition, the proposed method is able to identify the fault categories and health states of rolling bearings simultaneously. The proposed damage detection methodology provides a new and better framework for intelligent fault diagnosis of rolling bearings in rotating machinery.

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Bi-filter multiscale-diversity-entropy-based weak feature extraction for a rotor-bearing system

Cited by 5 publications

References 34 publications

A novel cross-domain adaption network based on Se-Sk-DenseNet for remaining useful life prediction of rolling bearings under different working conditions

A novel cross-domain adaption network based on Se-Sk-DenseNet for remaining useful life prediction of rolling bearings under different working conditions

Application of an Improved Laplacian-of-Gaussian Filter for Bearing Fault Signal Enhancement of Motors

Intelligent Fault Diagnosis of Rolling Bearings Based on a Complete Frequency Range Feature Extraction and Combined Feature Selection Methodology

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