Anomaly-informed remaining useful life estimation (AIRULE) of bearing machinery using deep learning framework

Abstract

A rolling bearing is a crucial element within rotating machinery, and its smooth operation profoundly influences the overall well-being of the equipment. Consequently, analyzing its operational condition is crucial to prevent production losses or, in extreme cases, potential fatalities due to catastrophic failures. Accurate estimates of the Remaining Useful Life (RUL) of rolling bearings ensure manufacturing safety while also leading to cost savings. • This paper proposes an intelligent deep learning-based framework for remaining useful life estimation of bearings on the basis of informed detection of anomalies. • The paper demonstrates the setup of an experimental bearing test rig and the collection of bearing condition monitoring data such as vibration data. • Advanced hybrid models of Encoder-Decoder LSTM demonstrate high forecasting accuracy in RUL estimation.

Keywords

• Anomaly-Informed Remaining Useful Life Estimation (AIRULE) using Hybrid LSTM models