State-of-the-Art Review into Signal Processing and Artificial Intelligence-based Approaches Applied in Gearbox Defect Diagnosis

Dubaish, Asaad; Jaber, Alaa

doi:10.30684/etj.2023.142462.1535

ETJ

2023

DOI: 10.30684/etj.2023.142462.1535

|View full text |Cite

State-of-the-Art Review into Signal Processing and Artificial Intelligence-based Approaches Applied in Gearbox Defect Diagnosis

Asaad Dubaish,

Alaa Jaber

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Enhanced Fault Detection of Wind Turbine Using eXtreme Gradient Boosting Technique Based on Nonstationary Vibration Analysis

Ogaili,

Hamzah,

Jaber

2024

J Fail. Anal. and Preven.

View full text Add to dashboard Cite

Enhanced Fault Detection of Wind Turbine Using eXtreme Gradient Boosting Technique Based on Nonstationary Vibration Analysis

Ogaili,

Hamzah,

Jaber

2024

J Fail. Anal. and Preven.

View full text Add to dashboard Cite

Multibranch Time-Frequency Spectrum Segmentation Approach for Gearbox Fault Diagnosis

Song,

Sun,

Gao

et al. 2024

IEEE Sensors J.

View full text Add to dashboard Cite

Automated wind turbines gearbox condition monitoring: A comparative study of machine learning techniques based on vibration analysis

Ogaili,

Mohammed,

Jaber

et al. 2024

FME Transactions

View full text Add to dashboard Cite

Wind turbines play a role in the adoption of renewable energy production, but they are susceptible to shutdowns that require thorough monitoring. Gearbox failures are an issue leading to maintenance and operational downtime. This study investigates the application of machine learning methods to enhance the diagnosis of gearbox problems using vibration analysis. Through the application of fault scenarios that impact bearings and gears, the researchers successfully extracted time domain features from vibration data of a 750 kW turbine testbed in order to detect indications of damage. Support Vector Machine (SVM), Naive Bayes, and K Nearest Neighbour (KNN) machine learning models were used to classify gearbox faults. Among these models, Naive Bayes achieved an accuracy rate of 95.7%, which exceeded the established benchmarks. The probabilistic approach was able to successfully associate symptom characteristics with fault patterns. Intelligent monitoring systems could improve maintenance efficiency. This data-driven approach highlights the potential of machine learning in supporting wind power development by eliminating gearbox inefficiencies and improving turbine reliability, and further research is being conducted to ensure that this approach works in concert with diversity and in the real world. This shows how machine learning is contributing to advances in renewable energy by helping to analyze predictive problems and prevent costly gearbox failures.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

State-of-the-Art Review into Signal Processing and Artificial Intelligence-based Approaches Applied in Gearbox Defect Diagnosis

Cited by 3 publications

References 49 publications

Enhanced Fault Detection of Wind Turbine Using eXtreme Gradient Boosting Technique Based on Nonstationary Vibration Analysis

Enhanced Fault Detection of Wind Turbine Using eXtreme Gradient Boosting Technique Based on Nonstationary Vibration Analysis

Multibranch Time-Frequency Spectrum Segmentation Approach for Gearbox Fault Diagnosis

Automated wind turbines gearbox condition monitoring: A comparative study of machine learning techniques based on vibration analysis

Contact Info

Product

Resources

About