Intelligent Condition Monitoring of Industrial Plants: An Overview of Methodologies and Uncertainty Management Strategies
Condition monitoring plays a significant role in the safety and reliability of modern industrial
systems. Artificial intelligence (AI) approaches are gaining attention from academia and …
systems. Artificial intelligence (AI) approaches are gaining attention from academia and …
Deep continuous convolutional networks for fault diagnosis
Convolutional neural network (CNN) architectures have been extensively utilized in data-
driven fault diagnosis and have demonstrated significant success. However, there remain …
driven fault diagnosis and have demonstrated significant success. However, there remain …
An industrial process fault diagnosis method based on independent slow feature analysis and stacked sparse autoencoder network
C Li, C Wen, Z Zhou - Journal of the Franklin Institute, 2024 - Elsevier
Deep learning, with its powerful multilayer nonlinear representation of deep neural
networks, enables models trained based on deep learning to describe the true distribution of …
networks, enables models trained based on deep learning to describe the true distribution of …
Data-driven Machinery Fault Detection: A Comprehensive Review
In this era of advanced manufacturing, it's now more crucial than ever to diagnose machine
faults as early as possible to guarantee their safe and efficient operation. With the massive …
faults as early as possible to guarantee their safe and efficient operation. With the massive …
Data-driven fault detection framework for offshore wind-hydrogen systems
T Zhao, S Feng, Y Zhou, Z Kang, J Kang - International Journal of …, 2024 - Elsevier
Offshore wind-hydrogen systems operate in harsh marine environments for extended
periods, posing risks of low accessibility and high failure rates. This paper proposes a data …
periods, posing risks of low accessibility and high failure rates. This paper proposes a data …
An optimized CNN-BiLSTM network for bearing fault diagnosis under multiple working conditions with limited training samples
Aiming at limitations in fully exploiting the temporal correlation features of the original
signals, expensive cost in parameter tuning, and difficulties in obtaining sufficient training …
signals, expensive cost in parameter tuning, and difficulties in obtaining sufficient training …
An intelligent framework of upgraded CapsNets with massive transmissibility data for identifying damage in bridges
Structural monitoring systems installed on bridges are capable of capturing large-scale
dynamic responses online and in real-time. The response data of the bridge under different …
dynamic responses online and in real-time. The response data of the bridge under different …
OHCA-GCN: A novel graph convolutional network-based fault diagnosis method for complex systems via supervised graph construction and optimization
Fault diagnosis is crucial for ensuring the safe and stable operation of complex systems.
Recently, graph convolutional network (GCN)-based fault diagnosis method has emerged …
Recently, graph convolutional network (GCN)-based fault diagnosis method has emerged …
Prior Knowledge-Augmented Meta-Learning for Fine-Grained Fault Diagnosis
In existing fault diagnosis methods, fault categories are generally coarse-grained, which
may result in failure to precisely identify fault details. Therefore, fine-grained fault diagnosis …
may result in failure to precisely identify fault details. Therefore, fine-grained fault diagnosis …
Industrial process fault diagnosis based on domain adaptive broad echo network
M Mou, X Zhao - Journal of the Taiwan Institute of Chemical Engineers, 2024 - Elsevier
Background In response to the challenge that traditional fault diagnosis models are difficult
to maintain satisfactory accuracy when data distribution changes due to changes in process …
to maintain satisfactory accuracy when data distribution changes due to changes in process …