As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …

Abstract Machine learning (ML) pervades an increasing number of academic disciplines and
industries. Its impact is profound, and several fields have been fundamentally altered by it …

We are performing the digital transition of industry, living the 4th industrial revolution,
building a new World in which the digital, physical and human dimensions are interrelated in …

Intelligent machinery prognostics and health management (PHM) methods have been
attracting growing attention in the past years, with the rapid development of the artificial …

Traditionally, system prognostics and health management (PHM) depends on sufficient prior
knowledge of critical components degradation process in order to predict the remaining …

The purpose of this article is to present a structured review of publications utilizing machine
learning methods to aid in engineering risk assessment. A keyword search is performed to …

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

Intelligent data-driven system prognostic methods have been popularly developed in the
recent years. Despite the promising results, most approaches assume the training and …

Artificial neural networks (ANNs) based machine learning models and especially deep
learning models have been widely applied in computer vision, signal processing, wireless …

Accurate remaining useful life (RUL) prediction of critical assets is an important challenge in
condition based maintenance to improve reliability and decrease machine's breakdown and …