The increasing availability of condition-monitoring data for components/systems has
incentivized the development of data-driven Remaining Useful Life (RUL) prognostics in the …

Abstract Most Remaining Useful Life (RUL) prognostics are obtained using supervised
learning models trained with many labelled data samples (ie, the true RUL is known). In …

Abstract Prognostic Health Management aims to predict the Remaining Useful Life (RUL) of
degrading components/systems utilizing monitoring data. These RUL predictions form the …

A good weight initialization is crucial to accelerate the convergence of the weights in a
neural network. However, training a neural network is still time-consuming, despite recent …

This review paper comprehensively analyzes the prognosis of rotating machines (RMs),
focusing on mechanical-flaw and remaining-useful-life (RUL) estimation in industrial and …

Predictive maintenance (PdM) is critical to ensure optimal operating efficiency and minimize
costly failures of industrial machinery. The PdM leverages a machine learning (ML) method …

Real-time and accurate prediction of remaining useful life (RUL) is important to safe
operation and maintenance (O&M) planning of mechanical equipment. However, the …

In many settings, fleets of assets must perform series of missions with in-between finite
breaks. For such fleets, a widely used maintenance strategy is the fleet selective …

Mechanical systems are crucial for the safety of complex equipment. As they often operate in
harsh environments, the patterns of their failures are becoming increasingly difficult to grasp …

Abstract Predicting the Remaining Useful Life (RUL) of mechanical systems poses
significant challenges in Prognostics and Health Management (PHM), impacting safety and …