[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 …
Challenges and opportunities of deep learning-based process fault detection and diagnosis: a review
J Yu, Y Zhang - Neural Computing and Applications, 2023 - Springer
Process fault detection and diagnosis (FDD) is a predominant task to ensure product quality
and process reliability in modern industrial systems. Those traditional FDD techniques are …
and process reliability in modern industrial systems. Those traditional FDD techniques are …
Interaction-aware graph neural networks for fault diagnosis of complex industrial processes
Fault diagnosis of complex industrial processes becomes a challenging task due to various
fault patterns in sensor signals and complex interactions between different units. However …
fault patterns in sensor signals and complex interactions between different units. However …
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 …
Interval-aware probabilistic slow feature analysis for irregular dynamic process monitoring with missing data
Due to unexpected data transition or equipment failures, irregular data with missing values,
which have both irregular sampling intervals and missing values, become very common in …
which have both irregular sampling intervals and missing values, become very common in …
A multiphase information fusion strategy for data-driven quality prediction of industrial batch processes
As one of the most important modes of industrial production, the batch process often
involves complex and continuous physicochemical reactions, making it challenging to …
involves complex and continuous physicochemical reactions, making it challenging to …
Deeppipe: A semi-supervised learning for operating condition recognition of multi-product pipelines
Intelligent operating monitoring of pipelines helps to detect anomalies in time to ensure
pipeline safe, reducing potential risk. However, the operating conditions of the multi-product …
pipeline safe, reducing potential risk. However, the operating conditions of the multi-product …
A distributed principal component regression method for quality-related fault detection and diagnosis
Modern industrial processes are confronted with a large-scale challenge in recent years. In
this paper, a novel distributed kernel principal component regression (DKPCR) approach is …
this paper, a novel distributed kernel principal component regression (DKPCR) approach is …
Principal component analysis-based ensemble detector for incipient faults in dynamic processes
The significant advancement in data-driven fault detection has been made, but incipient
faults such as faults 3, 9, and 15 in Tennessee Eastern process (TEP) still remain difficult for …
faults such as faults 3, 9, and 15 in Tennessee Eastern process (TEP) still remain difficult for …
Deep learning feature-based setpoint generation and optimal control for flotation processes
M Ai, Y Xie, Z Tang, J Zhang, W Gui - Information Sciences, 2021 - Elsevier
Computer vision-based control is a nonintrusive, cost-effective, and reliable technique for
flotation process control. It is known that deep learning features can depict the complex …
flotation process control. It is known that deep learning features can depict the complex …