A DEEP LONG SHORT-TERM MEMORY NETWORK FOR BEARING FAULT DIAGNOSIS UNDER TIME-VARYING CONDITIONS
Department of Mechanical Engineering-Engineering Mechanics
The fault diagnosis of bearing in machinery system plays a vital role in ensuring the normal operating performance of system. Machine learning-based fault diagnosis using vibration measurement recently has become a prevailing approach, which aims at identifying the fault through exploring the correlation between the measurement and respective fault. Nevertheless, such correlation will become very complex for the practical scenario where the system is operated under time-varying conditions. To fulfill the reliable bearing fault diagnosis under time-varying condition, this study presents a tailored deep learning model, so called deep long short-term memory (LSTM) network. By fully exploiting the strength of this model in characterizing the temporal dependence of time-series vibration measurement, the negative consequence of time-varying conditions can be minimized, thereby improving the diagnosis performance. The published bearing dataset with various time-varying operating speeds is utilized in case illustrations to validate the effectiveness of proposed methodology.
Proceedings of the ASME Design Engineering Technical Conference
A DEEP LONG SHORT-TERM MEMORY NETWORK FOR BEARING FAULT DIAGNOSIS UNDER TIME-VARYING CONDITIONS.
Proceedings of the ASME Design Engineering Technical Conference,
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