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 kernel methods for feature extraction in nonlinear process monitoring
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 …
Review and perspectives of data-driven distributed monitoring for industrial plant-wide processes
Process monitoring is crucial for maintaining favorable operating conditions and has
received considerable attention in previous decades. Currently, a plant-wide process …
received considerable attention in previous decades. Currently, a plant-wide process …
[HTML][HTML] Multi-fault diagnosis for series-connected lithium-ion battery pack with reconstruction-based contribution based on parallel PCA-KPCA
Various faults of the lithium-ion battery threaten the safety and performance of the battery
system. The early faults are difficult to detect and isolate owing to unobvious abnormality …
system. The early faults are difficult to detect and isolate owing to unobvious abnormality …
Local–global modeling and distributed computing framework for nonlinear plant-wide process monitoring with industrial big data
Industrial big data and complex process nonlinearity have introduced new challenges in
plant-wide process monitoring. This article proposes a local-global modeling and distributed …
plant-wide process monitoring. This article proposes a local-global modeling and distributed …
Fault monitoring using novel adaptive kernel principal component analysis integrating grey relational analysis
Y Han, G Song, F Liu, Z Geng, B Ma, W Xu - Process Safety and …, 2022 - Elsevier
The kernel principal component analysis (KPCA) is widely used as a fault monitoring tool for
complex nonlinear chemical processes in recent years. The cumulative contribution rate that …
complex nonlinear chemical processes in recent years. The cumulative contribution rate that …
Comparing PCA-based fault detection methods for dynamic processes with correlated and Non-Gaussian variables
MA de Carvalho Michalski, GFM de Souza - Expert Systems with …, 2022 - Elsevier
Maintenance strategies have been playing an increasingly important role in improving
engineering systems' performance, supporting the growth of availability and reliability, and …
engineering systems' performance, supporting the growth of availability and reliability, and …
Prognostics and health management in nuclear power plants: An updated method-centric review with special focus on data-driven methods
In a carbon-constrained world, future uses of nuclear power technologies can contribute to
climate change mitigation as the installed electricity generating capacity and range of …
climate change mitigation as the installed electricity generating capacity and range of …
Fault detection and diagnosis strategy based on k-nearest neighbors and fuzzy C-means clustering algorithm for industrial processes
Fault detection and diagnosis is crucial in recent industry sector to ensure safety and
reliability, and improve the overall equipment efficiency. Moreover, fault detection and …
reliability, and improve the overall equipment efficiency. Moreover, fault detection and …
Research on the fault monitoring method of marine diesel engines based on the manifold learning and isolation forest
In this paper, an innovative hybrid fault monitoring scheme integrating the manifold learning
and the isolation forest was established to monitor the state of marine diesel engine. The …
and the isolation forest was established to monitor the state of marine diesel engine. The …