Deep learning techniques in intelligent fault diagnosis and prognosis for industrial systems: a review
S Qiu, X Cui, Z Ping, 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 …
[HTML][HTML] A survey on machine learning based analysis of heterogeneous data in industrial automation
In many application domains data from different sources are increasingly available to
thoroughly monitor and describe a system or device. Especially within the industrial …
thoroughly monitor and describe a system or device. Especially within the industrial …
[HTML][HTML] A two-stage importance-aware subgraph convolutional network based on multi-source sensors for cross-domain fault diagnosis
Graph convolutional networks (GCNs) as the emerging neural networks have shown great
success in Prognostics and Health Management because they can not only extract node …
success in Prognostics and Health Management because they can not only extract node …
Physics-informed gated recurrent graph attention unit network for anomaly detection in industrial cyber-physical systems
W Wu, C Song, J Zhao, Z Xu - Information Sciences, 2023 - Elsevier
Industrial cyber-physical systems (ICPSs) play an important role in many critical
infrastructures. To ensure the secure and reliable operation of ICPSs, this work presents a …
infrastructures. To ensure the secure and reliable operation of ICPSs, this work presents a …
Self-adaptation graph attention network via meta-learning for machinery fault diagnosis with few labeled data
Effective application of fault diagnosis models requires that new fault types can be
recognized rapidly after they occur few times, even only one time. To this end, a self …
recognized rapidly after they occur few times, even only one time. To this end, a self …
Causal intervention graph neural network for fault diagnosis of complex industrial processes
With the development of industry and manufacturing, the mechanical structures of
equipment have become intricate and complex. Due to the interaction between components …
equipment have become intricate and complex. Due to the interaction between components …
An evolutionary game with the game transitions based on the Markov process
The psychology of the individual is continuously changing in nature, which has a significant
influence on the evolutionary dynamics of populations. To study the influence of the …
influence on the evolutionary dynamics of populations. To study the influence of the …
Global prior transformer network in intelligent borescope inspection for surface damage detection of aeroengine blade
Surface damage detection is vital for diagnosis and monitoring of aeroengine blade. At
present, borescope inspection is the dominant technology. Several inspectors hold …
present, borescope inspection is the dominant technology. Several inspectors hold …
Physics-constraint variational neural network for wear state assessment of external gear pump
W Xu, Z Zhou, T Li, C Sun, X Chen… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Most current data-driven prognosis approaches suffer from their uncontrollable and
unexplainable properties. To address this issue, this article proposes a physics-constraint …
unexplainable properties. To address this issue, this article proposes a physics-constraint …
STAGED: A spatial-temporal aware graph encoder–decoder for fault diagnosis in industrial processes
Data-driven fault diagnosis for critical industrial processes has exhibited promising potential
with massive operating data from the supervisory control and data acquisition system …
with massive operating data from the supervisory control and data acquisition system …