Machinery prognostics is one of the major tasks in condition based maintenance (CBM),
which aims to predict the remaining useful life (RUL) of machinery based on condition …

Wind turbines play an increasingly important role in renewable power generation. To ensure
the efficient production and financial viability of wind power, it is crucial to maintain wind …

Remaining useful life (RUL) prediction, allowing for mechanical prediction maintenance,
reduces the unplanned expensive maintenance greatly. Deep learning methods have …

Induction motors most often fail due to faults in the rolling element bearings. Such failures
can cause long and unscheduled downtime in a production facility, which can result in huge …

An automated remaining useful life (RUL) prediction technique based on a deep learning
network is proposed in this study for an end-to-end RUL prediction of rolling element …

Remaining useful life (RUL) prediction can effectively avoid unexpected mechanical
breakdowns, thus improving operational reliability. However, the distribution discrepancy …

Remaining useful life (RUL) prediction is a vital task in rolling bearing prognostics and
health management (PHM) process. Kalman filtering (KF) is one of the hot spots in the …

In recent years, data-driven approaches for remaining useful life (RUL) prognostics have
aroused widespread concern. Bearings act as the fundamental component of machinery …

Tool wear is a key factor affecting many aspects of metal cutting machining, including
surface quality, machining efficiency and tool life. As machining continues to evolve towards …

Remaining useful life (RUL) prediction of rolling bearings is crucial to equipment operation
and maintenance. The data-driven Wiener-based methods have aroused widespread …