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

Internal radial clearance is a key factor influencing bearing fatigue life. Moreover, bearings
inevitably suffer from various wears and tears, which result in gradual increase of clearance …

Improving railway safety depends heavily on the reliability of railway turnouts. The
realization of effective, reliable and continuous observations for the spatial analysis and …

Accurate remaining useful life prognosis of bearings in wind turbines is beneficial for
operation and maintenance schedule at wind farms. The major technical challenges include …

This paper proposes the gated recurrent unit (GRU)‐recurrent neural network (RNN), a deep
learning approach to predict the remaining useful life (RUL) of lithium‐ion batteries (LIBs) …

Accurate monitoring of degradation in bearing is essential for preventing unexpected
shutdown of a machinery system. This paper proposes a novel health degradation indicator …

The railway industry has the potential to make a strong contribution to the achievement of
various sustainable development goals, by an expansion of its role in the transportation …

Remaining useful life (RUL) prediction has been a difficult problem in industry and
academia. Deep learning techniques based on supervised learning have made great …

Industrial Internet of Things has significantly boosted predictive maintenance for complex
industrial systems, where the accurate prediction of remaining useful life (RUL) with high …

Along with wide application of sensors, multi-dimensional time-series data are commonly
available for remaining useful life (RUL) estimation. This paper proposes a joint data-driven …