Remaining useful life (RUL) prediction, known as 'prognostics', has long been recognized as
one of the key technologies in prognostics and health management (PHM) to maintain the …

Abstract Approaches such as Cyber-Physical Systems (CPS), Internet of Things (IoT),
Internet of Services (IoS), and Data Analytics have built a new paradigm called Industry 4.0 …

Deep learning applications have been thriving over the last decade in many different
domains, including computer vision and natural language understanding. The drivers for the …

Maintenance decisions in domains such as aeronautics are becoming increasingly
dependent on being able to predict the failure of components and systems. When data …

Hydrogen fuel cells are promising power sources that directly transform the chemical energy
produced by the chemical reaction of hydrogen and oxygen into electrical energy. However …

Accurate and reliable remaining useful life (RUL) assessment result provides decision-
makers valuable information to take suitable maintenance strategy to maximize the …

The Industry 4.0 paradigm is boosting the relevance of predictive maintenance (PdM) for
manufacturing and production industries. PdM strongly relies on Internet of Things (IoT) …

The past decade has witnessed the adoption of artificial intelligence (AI) in various
applications. It is of no exception in the area of prognostics and health management (PHM) …

Abstract In Prognostics and Health Management (PHM) sufficient prior observed
degradation data is usually critical for Remaining Useful Lifetime (RUL) prediction. Most …

In numerous industrial applications where safety, efficiency, and reliability are among
primary concerns, condition-based maintenance (CBM) is often the most effective and …