Early detection of faulty patterns and timely scheduling of maintenance events can minimize
risk to the underlying processes and increase a system's lifespan, reliability, and availability …

Abstract Machine-learning-based predictive maintenance models, ie models that predict
breakdowns of machines based on condition information, have a high potential to minimize …

The lack of bearing run-to-failure data has been one of the challenges in developing and
practically implementing robust bearing prognostics models. This paper proposes a new …

Lane detection is necessary for developing intelligent Autonomous Vehicles (AVs). Using
vision-based lane detection is more cost-effective, requiring less operational power. Images …

The success of rotating machines' data-driven remaining useful life (RUL) prognosis
approaches depends heavily on the abundance of entire life cycle data. However, it is not …

Predicting the remaining useful lifetime (RUL) of bearings in electric motors is crucial to
reduce repair costs in industrial maintenance. With the technological advances of Industry …

Over the past decades, deep learning techniques have attracted increased attention from
various research and industrial domains aligned with the development of Industry Internet-of …

Accurate remaining useful life (RUL) prediction of bearings is the key to effective decision‐
making for predictive maintenance (PdM) of rotating machinery. However, the individual …

Predictive maintenance (PdM) has tremendous potential for reducing total operational costs
and turnaround time in industrial manufacturing and maintenance services. In recent years …

The critical challenge of estimating the Remaining Useful Life (RUL) of MoSi2 heating
elements utilized in pusher kiln processes is to enhance operational efficiency and minimize …