Progress in root cause and fault propagation analysis of large-scale industrial processes
Fan Yang,Deyun Xiao +1 more
TLDR
In large-scale industrial processes, a fault can easily propagate between process units due to the interconnections of material and information flows so the problem of fault detection and isolation for these processes is more concerned about the root cause and fault propagation before applying quantitative methods in local models.Abstract:
In large-scale industrial processes, a fault can easily propagate between process units due to the interconnections of material and information flows. Thus the problem of fault detection and isolation for these processes is more concerned about the root cause and fault propagation before applying quantitative methods in local models. Process topology and causality, as the key features of the process description, need to be captured from process knowledge and process data. The modelling methods from these two aspects are overviewed in this paper. From process knowledge, structural equation modelling, various causal graphs, rule-based models, and ontological models are summarized. From process data, cross-correlation analysis, Granger causality and its extensions, frequency domain methods, information-theoretical methods, and Bayesian nets are introduced. Based on these models, inference methods are discussed to find root causes and fault propagation paths under abnormal situations. Some future work is proposed in the end.read more
Citations
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Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data
TL;DR: A systematic review of various state-of-the-art data preprocessing tricks as well as robust principal component analysis methods for process understanding and monitoring applications and big data perspectives on potential challenges and opportunities have been highlighted.
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Partial directed coherence: a new concept in neural structure determination
Koichi Sameshima,Luiz A. Baccalá +1 more
TL;DR: A new frequency-domain approach to describe the relationships (direction of information flow) between multivariate time series based on the decomposition of multivariate partial coherences computed from multivariate autoregressive models is introduced.
Book
Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
Chris Aldrich,Lidia Auret +1 more
TL;DR: This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods.
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Root cause detection in a service-oriented architecture
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Methods for root cause diagnosis of plant‐wide oscillations
TL;DR: Five process topology-based methods are compared and shown to be capable of finding oscillation propagation pathways and, thus, help in determining the root cause of plant-wide oscillations by application to an industrial benchmark dataset.
References
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