Data-driven fault diagnosis for traction systems in high-speed trains: A survey, challenges, and perspectives
Recently, to ensure the reliability and safety of high-speed trains, detection and diagnosis of
faults (FDD) in traction systems have become an active issue in the transportation area over …
faults (FDD) in traction systems have become an active issue in the transportation area over …
[HTML][HTML] A review on autoencoder based representation learning for fault detection and diagnosis in industrial processes
Process monitoring technologies play a key role in maintaining the steady state of industrial
processes. However, with the increasing complexity of modern industrial processes …
processes. However, with the increasing complexity of modern industrial processes …
Role of research and development in green economic growth through renewable energy development: empirical evidence from South Asia
W Fang, Z Liu, ARS Putra - Renewable Energy, 2022 - Elsevier
The study focuses on examining the impact of R&D and industrialization on green economic
growth. Financial assistance for environmentally friendly initiatives, the advancement of new …
growth. Financial assistance for environmentally friendly initiatives, the advancement of new …
A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network
Deep learning networks have been recently utilized for fault detection and diagnosis (FDD)
due to its effectiveness in handling industrial process data, which are often with high …
due to its effectiveness in handling industrial process data, which are often with high …
[HTML][HTML] A review on fault detection and process diagnostics in industrial processes
YJ Park, SKS Fan, CY Hsu - Processes, 2020 - mdpi.com
The main roles of fault detection and diagnosis (FDD) for industrial processes are to make
an effective indicator which can identify faulty status of a process and then to take a proper …
an effective indicator which can identify faulty status of a process and then to take a proper …
Deep convolutional neural network model based chemical process fault diagnosis
H Wu, J Zhao - Computers & chemical engineering, 2018 - Elsevier
Numerous accidents in chemical processes have caused emergency shutdowns, property
losses, casualties and/or environmental disruptions in the chemical process industry. Fault …
losses, casualties and/or environmental disruptions in the chemical process industry. Fault …
Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification
C Lu, ZY Wang, WL Qin, J Ma - Signal Processing, 2017 - Elsevier
Effective fault diagnosis has long been a research topic in the prognosis and health
management of rotary machinery engineered systems due to the benefits such as safety …
management of rotary machinery engineered systems due to the benefits such as safety …
Deep learning-based anomaly detection in cyber-physical systems: Progress and opportunities
Anomaly detection is crucial to ensure the security of cyber-physical systems (CPS).
However, due to the increasing complexity of CPSs and more sophisticated attacks …
However, due to the increasing complexity of CPSs and more sophisticated attacks …
Time series classification with multivariate convolutional neural network
CL Liu, WH Hsaio, YC Tu - IEEE Transactions on industrial …, 2018 - ieeexplore.ieee.org
Time series classification is an important research topic in machine learning and data
mining communities, since time series data exist in many application domains. Recent …
mining communities, since time series data exist in many application domains. Recent …
A novel dynamic PCA algorithm for dynamic data modeling and process monitoring
Principal component analysis (PCA) has been widely applied for data modeling and process
monitoring. However, it is not appropriate to directly apply PCA to data from a dynamic …
monitoring. However, it is not appropriate to directly apply PCA to data from a dynamic …