A review on fault detection and process diagnostics in industrial processes
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 …
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) …
Principal component analysis: A natural approach to data exploration
FL Gewers, GR Ferreira, HFD Arruda, FN Silva… - ACM Computing …, 2021 - dl.acm.org
Principal component analysis (PCA) is often applied for analyzing data in the most diverse
areas. This work reports, in an accessible and integrated manner, several theoretical and …
areas. This work reports, in an accessible and integrated manner, several theoretical and …
Canonical variate dissimilarity analysis for process incipient fault detection
KES Pilario, Y Cao - IEEE Transactions on Industrial …, 2018 - ieeexplore.ieee.org
Early detection of incipient faults in industrial processes is increasingly becoming important,
as these faults can slowly develop into serious abnormal events, an emergency situation, or …
as these faults can slowly develop into serious abnormal events, an emergency situation, or …
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 …
Machine learning for anomaly detection and process phase classification to improve safety and maintenance activities
E Quatrini, F Costantino, G Di Gravio… - Journal of Manufacturing …, 2020 - Elsevier
Anomaly detection is a crucial aspect for both safety and efficiency of modern process
industries. This paper proposes a two-steps methodology for anomaly detection in industrial …
industries. This paper proposes a two-steps methodology for anomaly detection in industrial …
A review of kernel methods for feature extraction in nonlinear process monitoring
KE Pilario, M Shafiee, Y Cao, L Lao, SH Yang - Processes, 2019 - mdpi.com
Kernel methods are a class of learning machines for the fast recognition of nonlinear
patterns in any data set. In this paper, the applications of kernel methods for feature …
patterns in any data set. In this paper, the applications of kernel methods for feature …
An adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window KPCA and information geometric causal inference
In recent years, fault detection and diagnosis for industrial processes have been rapidly
developed to minimize costs and maximize efficiency by taking advantages of cheap …
developed to minimize costs and maximize efficiency by taking advantages of cheap …
Machine learning-based statistical testing hypothesis for fault detection in photovoltaic systems
R Fazai, K Abodayeh, M Mansouri, M Trabelsi… - Solar Energy, 2019 - Elsevier
In this paper, we consider a machine learning approach merged with statistical testing
hypothesis for enhanced fault detection performance in photovoltaic (PV) systems. The …
hypothesis for enhanced fault detection performance in photovoltaic (PV) systems. The …
Online reduced kernel principal component analysis for process monitoring
R Fezai, M Mansouri, O Taouali, MF Harkat… - Journal of Process …, 2018 - Elsevier
Kernel principal component analysis (KPCA), which is a nonlinear extension of principal
component analysis (PCA), has gained significant attention as a monitoring method for …
component analysis (PCA), has gained significant attention as a monitoring method for …