Multireceptive field graph convolutional networks for machine fault diagnosis
Deep learning (DL) based methods have swept the field of mechanical fault diagnosis,
because of the powerful ability of feature representation. However, many of existing DL …
because of the powerful ability of feature representation. However, many of existing DL …
Bearing remaining useful life prediction using self-adaptive graph convolutional networks with self-attention mechanism
Bearings are commonly used to reduce friction between moving parts. Bearings may fail due
to lubrication failure, contamination, corrosion, and fatigue. To prevent bearing failures, it is …
to lubrication failure, contamination, corrosion, and fatigue. To prevent bearing failures, it is …
Fault detection for nonlinear dynamic systems with consideration of modeling errors: A data-driven approach
This article is concerned with data-driven realization of fault detection (FD) for nonlinear
dynamic systems. In order to identify and parameterize nonlinear Hammerstein models …
dynamic systems. In order to identify and parameterize nonlinear Hammerstein models …
Cross-domain open-set machinery fault diagnosis based on adversarial network with multiple auxiliary classifiers
Cross-domain fault diagnosis methods based on transfer learning attempt to leverage
knowledge from a domain with sufficient labeled samples to a different but related domain …
knowledge from a domain with sufficient labeled samples to a different but related domain …
Adversarial autoencoder based feature learning for fault detection in industrial processes
Deep learning has recently emerged as a promising method for nonlinear process
monitoring. However, ensuring that the features from process variables have representative …
monitoring. However, ensuring that the features from process variables have representative …
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 …
A single-side neural network-aided canonical correlation analysis with applications to fault diagnosis
Recently, canonical correlation analysis (CCA) has been explored to address the fault
detection (FD) problem for industrial systems. However, most of the CCA-based FD methods …
detection (FD) problem for industrial systems. However, most of the CCA-based FD methods …
Sensor drift fault diagnosis for chiller system using deep recurrent canonical correlation analysis and k-nearest neighbor classifier
L Gao, D Li, L Yao, Y Gao - ISA transactions, 2022 - Elsevier
Early detection and diagnosis of the chiller sensor drift fault are crucial to maintain normal
operation for energy saving. Due to the complex physical structure and operation conditions …
operation for energy saving. Due to the complex physical structure and operation conditions …
Data-driven fault detection for dynamic systems with performance degradation: A unified transfer learning framework
Continuous operations can result in performance degradation of industrial systems, which
naturally increases complexity in fault detection (FD). In this study, a transfer learning …
naturally increases complexity in fault detection (FD). In this study, a transfer learning …
A new key performance indicator oriented industrial process monitoring and operating performance assessment method based on improved Hessian locally linear …
H Zhang, C Zhang, J Dong, K Peng - International Journal of …, 2022 - Taylor & Francis
The industrial process monitoring and operating performance assessment techniques are of
great significance to ensure the safety and efficiency of the production and to improve the …
great significance to ensure the safety and efficiency of the production and to improve the …