TY - GEN
T1 - Enhanced Gear Damage Prediction Using Convolutional Neural Networks on Time-Series Sensor Data
AU - Mayo, Sohaib Arshad
AU - Cai, Zhiqiang
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Gearboxes are essential in various industries, serving as the core mechanism for power transmission in machines like wind turbines and vehicles. Traditional fault detection primarily uses vibration analysis, which, although effective, often fails to precisely quantify gear damage. This paper introduces a novel approach by integrating artificial intelligence (AI) with vibration data to enhance gearbox fault diagnosis. By employing machine learning algorithms, our model not only detects but also quantifies the severity of gear faults. This method involves collecting and analyzing vibration signals under different operational conditions to train a deep learning model that can accurately predict fault severity. This innovative approach, to the best of our knowledge, is the first to use AI to quantify gearbox faults, potentially revolutionizing maintenance strategies and improving industrial machinery reliability.
AB - Gearboxes are essential in various industries, serving as the core mechanism for power transmission in machines like wind turbines and vehicles. Traditional fault detection primarily uses vibration analysis, which, although effective, often fails to precisely quantify gear damage. This paper introduces a novel approach by integrating artificial intelligence (AI) with vibration data to enhance gearbox fault diagnosis. By employing machine learning algorithms, our model not only detects but also quantifies the severity of gear faults. This method involves collecting and analyzing vibration signals under different operational conditions to train a deep learning model that can accurately predict fault severity. This innovative approach, to the best of our knowledge, is the first to use AI to quantify gearbox faults, potentially revolutionizing maintenance strategies and improving industrial machinery reliability.
KW - component
KW - convolutional neural network (CNN)
KW - Deep Learning (DL)
KW - Machine Learning (ML)
KW - Neural Networks (NN)
KW - Vibration Analysis
UR - http://www.scopus.com/inward/record.url?scp=85219632245&partnerID=8YFLogxK
U2 - 10.1109/PHM-BEIJING63284.2024.10874743
DO - 10.1109/PHM-BEIJING63284.2024.10874743
M3 - 会议稿件
AN - SCOPUS:85219632245
T3 - 15th Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024
BT - 15th Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024
A2 - Wang, Huimin
A2 - Li, Steven
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024
Y2 - 11 October 2024 through 13 October 2024
ER -