Remaining Useful Life (RUL) Estimation is a predictive maintenance concept used in various fields, particularly in engineering, reliability engineering, and maintenance management. It involves predicting the future operational lifespan of a component, system, or equipment based on its current condition and historical performance data. The goal is to estimate how much useful life remains before the equipment is likely to fail or become unreliable.


RULENet: End-to-end Learning with the Dual-estimator for Remaining Useful Life Estimation

Remaining Useful Life (RUL) estimation is a key element in Predictive maintenance. System agnostic approaches which just utilize sensor and operational time series have gained popularity due to its ease of implementation. Due to the nature of measurement or degradation mechanisms, its accurate estimation is not always feasible. Existing methods suppose the range of RUL with feasible estimation is given from results at upstream tasks or prior knowledge. In this work, we propose the novel framework of end-to-end learning for RUL estimation, which is called RULENet. RULENet simultaneously optimizes its Dual-estimator for RUL estimation and its feasible range estimation. Experimental results on NASA C-MAPSS benchmark data show the superiority of the end-to-end framework.