Journal ArticleDOI
Integrating independent component analysis and local outlier factor for plant-wide process monitoring
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TLDR
The proposed scheme integrating ICA and LOF is more suitable for real industry where the monitoring variables are the mixture of Gaussian and non- Gaussian variables, whereas existing ICA-based schemes assume only non-Gaussian distribution.About:
This article is published in Journal of Process Control.The article was published on 2011-08-01. It has received 89 citations till now. The article focuses on the topics: Local outlier factor & Independent component analysis.read more
Citations
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Review on data-driven modeling and monitoring for plant-wide industrial processes
TL;DR: A systematic review on data-driven modeling and monitoring for plant-wide processes is presented in this paper, where the authors provide an overview of the state-of-the-art data processing and modeling procedures for the plantwide process monitoring.
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Design of inferential sensors in the process industry: A review of Bayesian methods
TL;DR: This paper provides a general introduction to the main steps involved in development and implementation of industrial inferential sensors, and presents an overview of the relevant Bayesian methods for inferential modeling.
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EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis
TL;DR: In this article, a multivariate and multiscale statistical process monitoring method is proposed with the aim of detecting incipient failures in large slewing bearings, where subjective influence plays a minor role.
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Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis
TL;DR: Simulation using the Tennessee Eastman benchmark process shows that the proposed method outperforms the traditional KPCA, in terms of fault detection performance and the results obtained also demonstrate the potential of the proposed fault identification approach.
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A semi-supervised convolutional neural network-based method for steel surface defect recognition
TL;DR: A semi-supervised learning method using the convolutional neural network (CNN) is proposed for steel surface defect recognition, which requires fewer labeled samples, and the unlabeled data can be used to help training.
References
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Journal ArticleDOI
Independent component analysis: algorithms and applications
Aapo Hyvärinen,Erkki Oja +1 more
TL;DR: The basic theory and applications of ICA are presented, and the goal is to find a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible.
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LOF: identifying density-based local outliers
TL;DR: This paper contends that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier, called the local outlier factor (LOF), and gives a detailed formal analysis showing that LOF enjoys many desirable properties.
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A plant-wide industrial process control problem
James J. Downs,E.F. Vogel +1 more
TL;DR: In this article, a model of an industrial chemical process for the purpose of developing, studying and evaluating process control technology is presented, which is well suited for a wide variety of studies including both plantwide control and multivariable control problems.
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Fault Detection and Diagnosis in Industrial Systems
TL;DR: The appearance of this book is quite timely as it provides a much needed state-of-the-art exposition on fault detection and diagnosis, a topic of much interest to industrialists.
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Disturbance detection and isolation by dynamic principal component analysis
TL;DR: This paper uses a well-known ‘time lag shift’ method to include dynamic behavior in the PCA model and demonstrates the effectiveness of the proposed methodology on the Tennessee Eastman process simulation.