A review of artificial intelligence methods for engineering prognostics and health management with implementation guidelines
KTP Nguyen, K Medjaher, DT Tran - Artificial Intelligence Review, 2023 - Springer
The past decade has witnessed the adoption of artificial intelligence (AI) in various
applications. It is of no exception in the area of prognostics and health management (PHM) …
applications. It is of no exception in the area of prognostics and health management (PHM) …
A review of data-driven fault detection and diagnosis methods: Applications in chemical process systems
N Md Nor, CR Che Hassan… - Reviews in Chemical …, 2020 - degruyter.com
Fault detection and diagnosis (FDD) systems are developed to characterize normal
variations and detect abnormal changes in a process plant. It is always important for early …
variations and detect abnormal changes in a process plant. It is always important for early …
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 …
A deep belief network based fault diagnosis model for complex chemical processes
Z Zhang, J Zhao - Computers & chemical engineering, 2017 - Elsevier
Data-driven methods have been regarded as desirable methods for fault detection and
diagnosis (FDD) of practical chemical processes. However, with the big data era coming …
diagnosis (FDD) of practical chemical processes. However, with the big data era coming …
The ALAMO approach to machine learning
ZT Wilson, NV Sahinidis - Computers & Chemical Engineering, 2017 - Elsevier
ALAMO is a computational methodology for learning algebraic functions from data. Given a
data set, the approach begins by building a low-complexity, linear model composed of …
data set, the approach begins by building a low-complexity, linear model composed of …
Fault detection in Tennessee Eastman process with temporal deep learning models
Automated early process fault detection and prediction remains a challenging problem in
industrial processes. Traditionally it has been done by multivariate statistical analysis of …
industrial processes. Traditionally it has been done by multivariate statistical analysis of …
Fault detection and pathway analysis using a dynamic Bayesian network
A dynamic Bayesian network (DBN) based fault detection, root cause diagnosis, and fault
propagation pathway identification scheme is proposed. The proposed methodology …
propagation pathway identification scheme is proposed. The proposed methodology …
A review on data‐driven learning approaches for fault detection and diagnosis in chemical processes
Fault detection and diagnosis for process plants has been an active area of research for
many years. This review presents a concise overview on supervised and unsupervised data …
many years. This review presents a concise overview on supervised and unsupervised data …
Bidirectional deep recurrent neural networks for process fault classification
In this study, a new approach for time series based condition monitoring and fault diagnosis
based on bidirectional recurrent neural networks is presented. The application of …
based on bidirectional recurrent neural networks is presented. The application of …
A multi-scale convolutional neural network based fault diagnosis model for complex chemical processes
Q Song, P Jiang - Process Safety and Environmental Protection, 2022 - Elsevier
The chemical production process is a special dynamic and complex system. It has the
characteristics of instability and danger, thus making safety management in the production …
characteristics of instability and danger, thus making safety management in the production …