[HTML][HTML] Predictive maintenance using digital twins: A systematic literature review
Context Predictive maintenance is a technique for creating a more sustainable, safe, and
profitable industry. One of the key challenges for creating predictive maintenance systems is …
profitable industry. One of the key challenges for creating predictive maintenance systems is …
Machine learning for reliability engineering and safety applications: Review of current status and future opportunities
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 …
industries. Its impact is profound, and several fields have been fundamentally altered by it …
The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study
Deep learning (DL)-based methods have advanced the field of Prognostics and Health
Management (PHM) in recent years, because of their powerful feature representation ability …
Management (PHM) in recent years, because of their powerful feature representation ability …
[HTML][HTML] Deep reinforcement learning for predictive aircraft maintenance using probabilistic remaining-useful-life prognostics
The increasing availability of sensor monitoring data has stimulated the development of
Remaining-Useful-Life (RUL) prognostics and maintenance planning models. However …
Remaining-Useful-Life (RUL) prognostics and maintenance planning models. However …
Remaining useful life prediction with partial sensor malfunctions using deep adversarial networks
In recent years, intelligent data-driven prognostic methods have been successfully
developed, and good machinery health assessment performance has been achieved …
developed, and good machinery health assessment performance has been achieved …
Machinery health prognostics: A systematic review from data acquisition to RUL prediction
Machinery prognostics is one of the major tasks in condition based maintenance (CBM),
which aims to predict the remaining useful life (RUL) of machinery based on condition …
which aims to predict the remaining useful life (RUL) of machinery based on condition …
Remaining useful life estimation in prognostics using deep convolution neural networks
Traditionally, system prognostics and health management (PHM) depends on sufficient prior
knowledge of critical components degradation process in order to predict the remaining …
knowledge of critical components degradation process in order to predict the remaining …
[HTML][HTML] Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture
In recent years, research has proposed several deep learning (DL) approaches to providing
reliable remaining useful life (RUL) predictions in Prognostics and Health Management …
reliable remaining useful life (RUL) predictions in Prognostics and Health Management …
Deep separable convolutional network for remaining useful life prediction of machinery
Deep learning is gaining attention in data-driven remaining useful life (RUL) prediction of
machinery because of its powerful representation learning ability. With the help of deep …
machinery because of its powerful representation learning ability. With the help of deep …
Bayesian deep-learning for RUL prediction: An active learning perspective
Deep learning (DL) has been intensively exploited for remaining useful life (RUL) prediction
in the recent decade. Although with high precision and flexibility, DL methods need sufficient …
in the recent decade. Although with high precision and flexibility, DL methods need sufficient …