[HTML][HTML] Data analytics and machine learning for smart process manufacturing: Recent advances and perspectives in the big data era
Safe, efficient, and sustainable operations and control are primary objectives in industrial
manufacturing processes. State-of-the-art technologies heavily rely on human intervention …
manufacturing processes. State-of-the-art technologies heavily rely on human intervention …
Survey on data-driven industrial process monitoring and diagnosis
SJ Qin - Annual reviews in control, 2012 - Elsevier
This paper provides a state-of-the-art review of the methods and applications of data-driven
fault detection and diagnosis that have been developed over the last two decades. The …
fault detection and diagnosis that have been developed over the last two decades. The …
Data mining and analytics in the process industry: The role of machine learning
Data mining and analytics have played an important role in knowledge discovery and
decision making/supports in the process industry over the past several decades. As a …
decision making/supports in the process industry over the past several decades. As a …
A data-driven Bayesian network learning method for process fault diagnosis
This paper presents a data-driven methodology for fault detection and diagnosis (FDD) by
integrating the principal component analysis (PCA) with the Bayesian network (BN). Though …
integrating the principal component analysis (PCA) with the Bayesian network (BN). Though …
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 …
Perspectives on nonstationary process monitoring in the era of industrial artificial intelligence
C Zhao - Journal of Process Control, 2022 - Elsevier
The development of the Internet of Things, cloud computing, and artificial intelligence has
given birth to industrial artificial intelligence (IAI) technology, which enables us to obtain fine …
given birth to industrial artificial intelligence (IAI) technology, which enables us to obtain fine …
Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data
Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …
Review of recent research on data-based process monitoring
Data-based process monitoring has become a key technology in process industries for
safety, quality, and operation efficiency enhancement. This paper provides a timely update …
safety, quality, and operation efficiency enhancement. This paper provides a timely update …
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 …
Statistical process monitoring as a big data analytics tool for smart manufacturing
With ever-accelerating advancement of information, communication, sensing and
characterization technologies, such as industrial Internet of Things (IoT) and high-throughput …
characterization technologies, such as industrial Internet of Things (IoT) and high-throughput …