C-ECAFormer: A new lightweight fault diagnosis framework towards heavy noise and small samples
In engineering practice, small-sample fault diagnosis of mechanical equipment towards
heavy noise interference poses great challenges for the existing Transformer based …
heavy noise interference poses great challenges for the existing Transformer based …
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 …
Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey
This paper presents a comprehensive review of the historical development, the current state
of the art, and prospects of data-driven approaches for industrial process monitoring. The …
of the art, and prospects of data-driven approaches for industrial process monitoring. The …
Gated recurrent unit-enhanced deep convolutional neural network for real-time industrial process fault diagnosis
When deep learning-based models are employed for the fault diagnosis of chemical
processes, problems of poor calculation accuracy and efficiency often occur in the scenarios …
processes, problems of poor calculation accuracy and efficiency often occur in the scenarios …
Variance-capturing forward-forward autoencoder (VFFAE): A forward learning neural network for fault detection and isolation of process data
Data-driven models have emerged as popular choices for fault detection and isolation (FDI)
in process industries. However, real-time updating of these models due to streaming data …
in process industries. However, real-time updating of these models due to streaming data …
CausalViT: Domain generalization for chemical engineering process fault detection and diagnosis
H Huang, R Wang, K Zhou, L Ning, K Song - Process Safety and …, 2023 - Elsevier
Fault detection and diagnosis (FDD) is a promising technology for safe operation, quality
control, and profitability improvement in chemical process systems. In practice, chemical …
control, and profitability improvement in chemical process systems. In practice, chemical …
A latent representation dual manifold regularization broad learning system with incremental learning capability for fault diagnosis
M Mou, X Zhao, K Liu, S Cao, Y Hui - Measurement Science and …, 2023 - iopscience.iop.org
Fault diagnosis models based on deep learning must spend a lot of time adjusting the model
structure and parameters for retraining upon the occurrence of a new fault. To address this …
structure and parameters for retraining upon the occurrence of a new fault. To address this …
Deep learning and heterogeneous signal fusion approach to precursor feature recognition and early warning of coal and gas outburst
B Li, E Wang, Z Shang, X Liu, Z Li, J Dong - Process Safety and …, 2023 - Elsevier
Coal and gas outburst is one of the main disasters during the production process of coal
mines. Accurate recognition and advanced early warning are crucial to effectively preventing …
mines. Accurate recognition and advanced early warning are crucial to effectively preventing …
A High-Precision Detection Model of Small Objects in Maritime UAV Perspective Based on Improved YOLOv5
Object detection by shipborne unmanned aerial vehicles (UAVs) equipped with electro-
optical (EO) sensors plays an important role in maritime rescue and ocean monitoring …
optical (EO) sensors plays an important role in maritime rescue and ocean monitoring …
SCCAM: Supervised contrastive convolutional attention mechanism for Ante-Hoc interpretable fault diagnosis with limited fault samples
In real industrial processes, fault diagnosis methods are required to learn from limited fault
samples since the procedures are mainly under normal conditions and the faults rarely …
samples since the procedures are mainly under normal conditions and the faults rarely …