State-space modeling and novel entropy-based health indicator for dynamic degradation monitoring of rolling element bearing

Kumar, Anil; Parkash, Chander; Vashishtha, Govind; Tang, Hesheng; Kundu, Pradeep; Xiang, Jiawei

doi:10.1016/j.ress.2022.108356

Cited by 48 publications

(15 citation statements)

References 34 publications

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“…In order to avoid these sensitive issues of rotating components, regular monitoring is essential to prevent sudden failure. Vibration monitoring can be used to aid in the early detection of faults [4][5][6][7]. A vibration signal is extremely complicated source of rotating components and vibration-based diagnosis approach merely focus on the evaluation of the system's critical components.…”

Section: Introductionmentioning

confidence: 99%

Non-parametric Ensemble Empirical Mode Decomposition for extracting weak features to identify bearing defects

et al. 2023

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Section: Introductionmentioning

confidence: 99%

Non-parametric Ensemble Empirical Mode Decomposition for extracting weak features to identify bearing defects

et al. 2023

Self Cite

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“…Bearings are subjected to high-temperature and high-speed complex environments under certain loads all year round; thus, it is difficult to obtain the fault characteristics in the traditional fault diagnosis and degradation assessment methods (Guo et al, 2022; Wan et al, 2022). Therefore, intelligent predictive fault diagnosis and assessment methods are an important guarantee for the evolution of machinery maintenance strategies from post-event maintenance to predictive maintenance (Kumar et al, 2022; Shi et al, 2023; Xu et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Bearing performance degradation assessment using adversarial fusion convolutional autoencoder based on multi-source information

Wang,

Zhou,

Wen

et al. 2023

Transactions of the Institute of Measurement and Control

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Bearing operation states will directly determine the performance of the equipment; thus, monitoring operation status and degradation indicators is the key to ensuring continuous and healthy operation of the equipment. However, most of the research uses single-source information data, which makes it difficult to model when dealing with multi-source information, complex data distribution, and noise. In this paper, a bearing performance degradation assessment method based on multi-source information is proposed to comprehensively utilize the data signals of different structures, spaces, types, and sources. First, the adversarial fusion convolutional autoencoder is constructed for obtaining the degradation index of the bearing, while the adversarial learning strategy is applied to achieve the effect of enhancing the robustness and sensitivity of the degradation indicators extracted by the network. Then the degradation index is input into the support vector data description to determine the fault anomalies of the degradation index adaptively and the fuzzy c-means algorithms to obtain the final rolling bearing performance degradation evaluation results. Through the verification results of two experiment datasets, it is found that the proposed model can achieve accurate evaluation and quantitative analysis of the performance degradation process of bearings. As a result, the entire network ensures the reconstruction accuracy of normal samples while simultaneously stretching the reconstruction error of abnormal samples to achieve accurate monitoring of degradation onset.

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“…Hence a health indicator (HI) was often constructed to monitor tool wear degradation. The machine learning or statistical models can be used to mapped the relationship between extracted HIs and tool wear [10]. For example, Proteau et al [11] presented a specific cutting energy HI to monitor the tool wear, which calculated the amount of energy required to remove 1 cm 3 of material.…”

Section: Introductionmentioning

confidence: 99%

A novel current sensor indicator enabled WAFTR model for tool wear prediction under variable operating conditions

Kundu

Luo

Qin

et al. 2022

Journal of Manufacturing Processes

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State-space modeling and novel entropy-based health indicator for dynamic degradation monitoring of rolling element bearing

Cited by 48 publications

References 34 publications

Non-parametric Ensemble Empirical Mode Decomposition for extracting weak features to identify bearing defects

Non-parametric Ensemble Empirical Mode Decomposition for extracting weak features to identify bearing defects

Bearing performance degradation assessment using adversarial fusion convolutional autoencoder based on multi-source information

A novel current sensor indicator enabled WAFTR model for tool wear prediction under variable operating conditions

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