Transfer learning-motivated intelligent fault diagnosis designs: A survey, insights, and perspectives
Over the last decade, transfer learning has attracted a great deal of attention as a new
learning paradigm, based on which fault diagnosis (FD) approaches have been intensively …
learning paradigm, based on which fault diagnosis (FD) approaches have been intensively …
Explainable intelligent fault diagnosis for nonlinear dynamic systems: From unsupervised to supervised learning
The increased complexity and intelligence of automation systems require the development
of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected …
of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected …
Active pantograph in high-speed railway: Review, challenges, and applications
The pantograph–catenary system (PCS) vibration causes the pantograph–catenary contact
force (PCCF) to fluctuate, deteriorating current collection quality and damaging the electrical …
force (PCCF) to fluctuate, deteriorating current collection quality and damaging the electrical …
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 …
Fault-tolerant soft sensors for dynamic systems
H Chen, B Huang - IEEE Transactions on Control Systems …, 2023 - ieeexplore.ieee.org
Unpredicted faults occurring in automation systems deteriorate the performance of soft
sensors and may even lead to incorrect results. To address the problem, this study develops …
sensors and may even lead to incorrect results. To address the problem, this study develops …
Convformer-NSE: A novel end-to-end gearbox fault diagnosis framework under heavy noise using joint global and local information
The application of convolutional neural network (CNN) has greatly promoted the scope and
scenario of intelligent fault diagnosis and brought about a significant improvement of …
scenario of intelligent fault diagnosis and brought about a significant improvement of …
LSTMED: An uneven dynamic process monitoring method based on LSTM and Autoencoder neural network
Due to the complicated production mechanism in multivariate industrial processes, different
dynamic features of variables raise challenges to traditional data-driven process monitoring …
dynamic features of variables raise challenges to traditional data-driven process monitoring …
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 …
Overview of fault prognosis for traction systems in high-speed trains: A deep learning perspective
K Zhong, J Wang, S Xu, C Cheng, H Chen - Engineering Applications of …, 2023 - Elsevier
As the “heart” of high-speed train, traction systems play an important role in the safe
operation of trains, of which the operation and maintenance level is still unable to meet the …
operation of trains, of which the operation and maintenance level is still unable to meet the …
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 …