TY - JOUR
T1 - High-accuracy gearbox fault detection using deep learning on vibrational data
AU - Arshad Mayo, Sohaib
AU - Rehman, Saud
AU - Cai, Zhiqiang
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85207831398&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/2853/1/012066
DO - 10.1088/1742-6596/2853/1/012066
M3 - 会议文章
AN - SCOPUS:85207831398
SN - 1742-6588
VL - 2853
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012066
T2 - 7th International Conference on Mechanical, Electric, and Industrial Engineering, MEIE 2024
Y2 - 21 May 2024 through 23 May 2024
ER -