Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges
The computerized simulations of physical and socio-economic systems have proliferated in
the past decade, at the same time, the capability to develop high-fidelity system predictive …
the past decade, at the same time, the capability to develop high-fidelity system predictive …
A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies
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 …
attention because of its promise to further optimize process design, quality control, health …
Physics-Informed Residual Network (PIResNet) for rolling element bearing fault diagnostics
Various deep learning methodologies have recently been developed for machine condition
monitoring recently, and they have achieved impressive success in bearing fault …
monitoring recently, and they have achieved impressive success in bearing fault …
Deep convolutional generative adversarial network with semi-supervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations
Fault detection and diagnosis of gear systems using vibration measurements play an
important role in ensuring their functional reliability and safety. Computational intelligence …
important role in ensuring their functional reliability and safety. Computational intelligence …
[HTML][HTML] Physics-informed machine learning: a comprehensive review on applications in anomaly detection and condition monitoring
Condition monitoring plays a vital role in ensuring the reliability and optimal performance of
various engineering systems. Traditional methods for condition monitoring rely on physics …
various engineering systems. Traditional methods for condition monitoring rely on physics …
Deep learning techniques in intelligent fault diagnosis and prognosis for industrial systems: a review
S Qiu, X Cui, Z **, N Shan, Z Li, X Bao, X Xu - Sensors, 2023 - mdpi.com
Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the
captured sensory data, and also predict their failures in advance, which can greatly help to …
captured sensory data, and also predict their failures in advance, which can greatly help to …
GTFE-Net: A gramian time frequency enhancement CNN for bearing fault diagnosis
Fault diagnosis of the bearing is vital for the safe and reliable operation of rotating machines
in the manufacturing industry. Convolutional neural networks (CNNs) have been popular in …
in the manufacturing industry. Convolutional neural networks (CNNs) have been popular in …
[HTML][HTML] Insights into modern machine learning approaches for bearing fault classification: A systematic literature review
Rolling bearings are essential components in a wide range of equipment, such as
aeroplanes, trains, and wind turbines. Bearing failure has the potential to result in complete …
aeroplanes, trains, and wind turbines. Bearing failure has the potential to result in complete …
Automatic detection and severity analysis of grape black measles disease based on deep learning and fuzzy logic
M Ji, Z Wu - Computers and Electronics in Agriculture, 2022 - Elsevier
Grape black measles disease may be one of the best known, longest studied and most
destructive of all plant diseases, which ultimately reduces productivity and quality of …
destructive of all plant diseases, which ultimately reduces productivity and quality of …
[HTML][HTML] Develo** health indicators and RUL prognostics for systems with few failure instances and varying operating conditions using a LSTM autoencoder
Abstract Most Remaining Useful Life (RUL) prognostics are obtained using supervised
learning models trained with many labelled data samples (ie, the true RUL is known). In …
learning models trained with many labelled data samples (ie, the true RUL is known). In …