Construction of health indicators for condition monitoring of rotating machinery: A review of the research
The condition monitoring (CM) of rotating machinery (RM) is an essential operation for
improving the reliability of mechanical systems. For this purpose, an efficient CM method that …
improving the reliability of mechanical systems. For this purpose, an efficient CM method that …
[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 …
Towards trustworthy rotating machinery fault diagnosis via attention uncertainty in transformer
To enable researchers to fully trust the decisions made by deep diagnostic models,
interpretable rotating machinery fault diagnosis (RMFD) research has emerged. Existing …
interpretable rotating machinery fault diagnosis (RMFD) research has emerged. Existing …
Wavelet transform for rotary machine fault diagnosis: 10 years revisited
As a multi-resolution analysis method rooted rigorously in mathematics, wavelet transform
(WT) has shown its great potential in rotary machine fault diagnosis, characterized by …
(WT) has shown its great potential in rotary machine fault diagnosis, characterized by …
Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework
Fault diagnosis is efficient to improve the safety, reliability, and cost-effectiveness of
industrial machinery. Deep learning has been extensively investigated in fault diagnosis …
industrial machinery. Deep learning has been extensively investigated in fault diagnosis …
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 …
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 …
Multisource domain feature adaptation network for bearing fault diagnosis under time-varying working conditions
Intelligent fault diagnosis methods based on domain adaptation (DA) have been extensively
employed for tackling domain shift problems, and the basic diagnosis tasks under time …
employed for tackling domain shift problems, and the basic diagnosis tasks under time …
Variational attention-based interpretable transformer network for rotary machine fault diagnosis
Y Li, Z Zhou, C Sun, X Chen… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Deep learning technology provides a promising approach for rotary machine fault diagnosis
(RMFD), where vibration signals are commonly utilized as input of a deep network model to …
(RMFD), where vibration signals are commonly utilized as input of a deep network model to …
Discriminative feature learning using a multiscale convolutional capsule network from attitude data for fault diagnosis of industrial robots
Effective fault diagnosis is important to ensure the reliability, safety, and efficiency of
industrial robots. This article proposes a simple yet effective data acquisition strategy based …
industrial robots. This article proposes a simple yet effective data acquisition strategy based …