Gear pitting severity level identification using binary segmentation methodology-Reference-Cited by-同舟云学术

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Gear pitting severity level identification using binary segmentation methodology

Published:2019-12-13 Issue:3 Volume:27 Page:
ISSN:1545-2255
Container-title:Structural Control and Health Monitoring
language:en
Short-container-title:Struct Control Health Monit

Author:

Kundu Pradeep¹^ORCID,Darpe Ashish K.²,Kulkarni Makarand S.³

Affiliation:

1. Vibration Research Laboratory, Department of Mechanical EngineeringIndian Institute of Technology Delhi New Delhi India

2. Department of Mechanical EngineeringIndian Institute of Technology Delhi New Delhi India

3. Department of Mechanical EngineeringIndian Institute of Technology Bombay Mumbai India

Publisher

Wiley

Subject

Mechanics of Materials,Building and Construction,Civil and Structural Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/stc.2478

Reference29 articles.

1. Application of Hermitian wavelet to crack fault detection in gearbox

2. Dynamic modeling of gearbox faults: A review

3. Crack growth modeling in spur gear tooth and its effect on mesh stiffness using extended finite element method

4. Fault estimation of wind turbines using combined adaptive and parameter estimation schemes

5. Detection and Advancement Monitoring of Distributed Pitting Failure in Gears

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1. Review of rotating machinery elements condition monitoring using acoustic emission signal;Expert Systems with Applications;2024-10

2. Development of data-driven, physics-based, and hybrid prognosis frameworks: a case study for gear remaining useful life prediction;Journal of Intelligent Manufacturing;2024-08-16

3. Detection of seeded fault and growing fault in rotating machinery under different speed scenarios using adaptive resonance-based signal sparse decomposition;Structural Health Monitoring;2024-06-23

4. Fundamental relationship between rolling contact fatigue driven surface damage and torsional fatigue;International Journal of Mechanical Sciences;2024-02

5. A novel hybrid physical AI-based strategy for fault severity estimation in spur gears with zero-shot learning;Mechanical Systems and Signal Processing;2023-12

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