Enhancing Gearbox Fault Diagnosis through Advanced Feature Engineering and Data Segmentation Techniques

Shukla, Khyati; Holderbaum, William; Theodoridis, Theodoros; Wei, Guowu

Abstract

Efficient gearbox fault diagnosis is crucial for the cost-effective maintenance and reliable operation of rotating machinery. Despite extensive research, effective fault diagnosis remains challenging due to the multitude of features available for classification. Traditional feature selection methods often fail to achieve optimal performance in fault classification tasks. This study introduces diverse ranking methods for selecting the relevant features and utilizes data segmentation techniques such as sliding, windowing, and bootstrapping to strengthen predictive model performance and scalability. A comparative analysis of these methods was conducted to identify the potential causes and future solutions. An evaluation of the impact of enhanced feature engineering and data segmentation on predictive maintenance in gearboxes revealed promising outcomes, with decision trees, SVM, and KNN models outperforming others. Additionally, within a fully connected network, windowing emerged as a more robust and efficient segmentation method compared to bootstrapping. Further research is necessary to assess the performance of these techniques across diverse datasets and applications, offering comprehensive insights for future studies in fault diagnosis and predictive maintenance.

Citation

Shukla, K., Holderbaum, W., Theodoridis, T., & Wei, G. (in press). Enhancing Gearbox Fault Diagnosis through Advanced Feature Engineering and Data Segmentation Techniques. Machines, 12(4), 261. https://doi.org/10.3390/machines12040261

• Journal Article Type: Article
• Acceptance Date: Apr 9, 2024
• Online Publication Date: Apr 14, 2024
• Deposit Date: Apr 16, 2024
• Publicly Available Date: Apr 29, 2024
• Journal: Machines
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 4
• Pages: 261
• Series ISSN: 2075-1702
• DOI: https://doi.org/10.3390/machines12040261
• Keywords: Electrical and Electronic Engineering; Industrial and Manufacturing Engineering; Control and Optimization; Mechanical Engineering; Computer Science (miscellaneous); Control and Systems Engineering