High-accuracy gearbox fault detection using deep learning on vibrational data

Sohaib Arshad Mayo, Saud Rehman, Zhiqiang Cai

科研成果: 期刊稿件会议文章同行评审

1 引用 (Scopus)

摘要

In the face of escalating demands for reliability in industrial machinery, this study introduces an advanced deep-learning model aimed at the early detection of faults in rotating gear systems. Addressing the need for timely fault diagnosis to prevent operational disruptions and financial setbacks, our work utilizes a novel neural network approach applied to vibrational data from Kaggle's Gearbox Fault Diagnosis Dataset. The methodology involves data preprocessing steps, including normalization and one-hot encoding, followed by training a sequential neural network with multiple dense layers and dropout for regularization. The model, trained using the Adam optimizer and evaluated over 50 epochs, significantly outperforms traditional machine learning techniques, achieving a remarkable 98.63% accuracy in distinguishing between healthy and compromised gear conditions. This research marks a significant stride in predictive maintenance, providing a robust foundation for future development toward prognostic health management systems that anticipate maintenance needs and ensure uninterrupted industrial productivity.

源语言英语
文章编号012066
期刊Journal of Physics: Conference Series
2853
1
DOI
出版状态已出版 - 2024
活动7th International Conference on Mechanical, Electric, and Industrial Engineering, MEIE 2024 - Hybrid, Yichang, 中国
期限: 21 5月 202423 5月 2024

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