Investigation on Multi-entropy and Multi-statistical Features Fusion Approach for Fault Detection in Rolling Bearing Using VMD

Singh, Abhishek Anand; Harikrishnan, C I; Tiwari, Sanjay; Sharma, Snehsheel

doi:10.1080/03772063.2022.2083707

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…To resolve this issue, a multi-scale approach was established by Costa in 2002. The multi-scale analysis is executed through the coarse-graining procedure (Singh et al, 2022). Coarse graining generates multi-scale data for the given series by calculating the mean values of adjacent data for each scale.…”

Section: Concept Of Multi-scale Entropymentioning

confidence: 99%

Residual signal–based condition monitoring of planetary gearbox using electrical signature analysis

Sharma

Tiwari

2023

Journal of Vibration and Control

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Electrical signature–based technique, due to its non-intrusive nature, is very useful for condition monitoring of rotary machines. Major challenge is the dominance of line frequency in the current signature. The present study focused on decreasing this dominance of the line frequency by obtaining the residual signals through autoregressive modeling. The residual signals are used further to extract the health-related features. The recently developed weighted multi-scale fluctuation-based dispersion entropy features are extracted as health indicators. The extracted health indicators are used for classification of different types of planetary gearbox faults. The results reflect that the proposed methodology has the potential for diagnosing different types of planetary gearbox faults with acceptable accuracy values.

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Section: Concept Of Multi-scale Entropymentioning

confidence: 99%

Residual signal–based condition monitoring of planetary gearbox using electrical signature analysis

Sharma

Tiwari

2023

Journal of Vibration and Control

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“…At first, many signal analysis-based methods were widely used for fault feature extraction whereas signal analysis-based methods without achieving fault diagnosis [3][4][5]. Next, the machine learning-based fault diagnosis method become a focus.…”

Section: Introductionmentioning

confidence: 99%

Time graph sub-domain adaption adversarial for fault diagnosis

Sun,

Yin,

et al. 2024

Meas. Sci. Technol.

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Graph Neural Network (GNN)-based fault diagnosis has attracted widespread attention. However, the existing measure metrics of domain distribution discrepancy always is single, the weight of different domains is unknown, and the scale of GNN-based feature extractor is unknown. To address these issues, a Time Graph Sub-Domain Adaption Adversarial for Fault Diagnosis (TGSDAA) is proposed in this paper. Firstly, a multi-scale time connection layer is proposed to extract the feature of the signal. Specifically, an Encode layer is proposed to construct the undirected graph. Next, a multi-receptive field cluster-graph convolution neural network is proposed to extract features of the graph. Finally, a sub-domain alignment with adversarial adaption is proposed to align different domains and achieve fault diagnosis. Two different datasets are used to verify the effectiveness of TGSDAA. The experimental results show that the average diagnosis accuracy of TGSDAA can improve 4% than other methods.

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Failure mode and effects analysis for submersible pump component using proportionate risk assessment model: a case study in the power plant of Agartala

Bhattacharjee

Hussain

Dey

et al. 2023

Int J Syst Assur Eng Manag

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Investigation on Multi-entropy and Multi-statistical Features Fusion Approach for Fault Detection in Rolling Bearing Using VMD

Cited by 4 publications

References 25 publications

Residual signal–based condition monitoring of planetary gearbox using electrical signature analysis

Residual signal–based condition monitoring of planetary gearbox using electrical signature analysis

Time graph sub-domain adaption adversarial for fault diagnosis

Failure mode and effects analysis for submersible pump component using proportionate risk assessment model: a case study in the power plant of Agartala

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