This paper provides a comprehensive literature review on Predictive Maintenance (PdM)
with emphasis on system architectures, purposes and approaches. In industry, any outages …

In this survey paper, we systematically summarize existing literature on bearing fault
diagnostics with deep learning (DL) algorithms. While conventional machine learning (ML) …

Despite the recent advances of intelligent data-driven fault diagnosis methods on rotating
machines, balanced training data for different machine health conditions are assumed in …

Improving the reliability of engineered systems is a crucial problem in many applications in
various engineering fields, such as aerospace, nuclear energy, and water declination …

Prognostics and health management (PHM) has become a crucial aspect of the
management of engineering systems and structures, where sensor hardware and decision …

Coastal wetlands are main components of the “blue carbon” ecosystems in coastal zones.
Salt-marsh biomass is especially important regarding climate-change mitigation. Generating …

In a carbon-constrained world, future uses of nuclear power technologies can contribute to
climate change mitigation as the installed electricity generating capacity and range of …

Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex
production environments, smart manufacturing as envisioned under Industry 4.0 aims to …

Deep learning techniques have become one of the main propellers for solving engineering
problems effectively and efficiently. For instance, Predictive Maintenance methods have …

The present work aimed at the problems of less negative samples and more positive
samples in rail fastener fault diagnosis and low detection accuracy of heavy manual patrol …