[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 …
Transfer learning algorithms for bearing remaining useful life prediction: A comprehensive review from an industrial application perspective
Accurate remaining useful life (RUL) prediction for rolling bearings encounters many
challenges such as complex degradation processes, varying working conditions, and …
challenges such as complex degradation processes, varying working conditions, and …
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …
essential layer of safety assurance that could lead to more principled decision making by …
Challenges and opportunities of AI-enabled monitoring, diagnosis & prognosis: A review
Abstract Prognostics and Health Management (PHM), including monitoring, diagnosis,
prognosis, and health management, occupies an increasingly important position in reducing …
prognosis, and health management, occupies an increasingly important position in reducing …
Interpretable Machine Learning: A brief survey from the predictive maintenance perspective
S Vollert, M Atzmueller… - 2021 26th IEEE …, 2021 - ieeexplore.ieee.org
In the field of predictive maintenance (PdM), machine learning (ML) has gained importance
over the last years. Accompanying this development, an increasing number of papers use …
over the last years. Accompanying this development, an increasing number of papers use …
A deep learning model for predictive maintenance in cyber-physical production systems using lstm autoencoders
Condition monitoring of industrial equipment, combined with machine learning algorithms,
may significantly improve maintenance activities on modern cyber-physical production …
may significantly improve maintenance activities on modern cyber-physical production …
A real-time adaptive model for bearing fault classification and remaining useful life estimation using deep neural network
Rolling element bearings are essential components of a wide variety of industrial machinery
and the leading cause of equipment failure. The prediction of Remaining Useful Life (RUL) …
and the leading cause of equipment failure. The prediction of Remaining Useful Life (RUL) …
An integrated deep multiscale feature fusion network for aeroengine remaining useful life prediction with multisensor data
Most RUL prediction methods can only extract single-scale features, ignoring significant
details at other scales and layers. These methods are all constructed using one type of …
details at other scales and layers. These methods are all constructed using one type of …
Data-driven remaining useful life estimation for milling process: sensors, algorithms, datasets, and future directions
An increase in unplanned downtime of machines disrupts and degrades the industrial
business, which results in substantial credibility damage and monetary loss. The cutting tool …
business, which results in substantial credibility damage and monetary loss. The cutting tool …
Domain adaptive remaining useful life prediction with transformer
Prognostic health management (PHM) has become a crucial part in building highly
automated systems, whose primary task is to precisely predict the remaining useful life …
automated systems, whose primary task is to precisely predict the remaining useful life …