Journal ArticleDOI
A syntactic pattern-recognition approach for process monitoring and fault diagnosis
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TLDR
A backpropagation-based neural network was trained to identify the presence of the appropriate primitives in a trend of noisy process data and a process grammar which can utilize both contextual and non-contextual information to perform error correction and explanation generation has been developed.About:
This article is published in Engineering Applications of Artificial Intelligence.The article was published on 1995-02-01. It has received 163 citations till now. The article focuses on the topics: Syntactic pattern recognition & Abstract process.read more
Citations
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Journal ArticleDOI
A Review of Process Fault Detection and Diagnosis Part I : Quantitative Model-Based Methods
TL;DR: This three part series of papers is to provide a systematic and comparative study of various diagnostic methods from different perspectives and broadly classify fault diagnosis methods into three general categories and review them in three parts.
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A review of process fault detection and diagnosis: Part III: Process history based methods
TL;DR: This final part discusses fault diagnosis methods that are based on historic process knowledge that need to be addressed for the successful design and implementation of practical intelligent supervisory control systems for the process industries.
Generalized feature extraction for structural pattern recognition in time-series data
TL;DR: The ability of the suite of structure detectors to generate features useful for structural pattern recognition is evaluated by comparing the classification accuracies achieved when using the structure detectors versus commonly-used statistical feature extractors, thus demonstrating that the suiteOf structure detectors effectively performs generalized feature extraction forStructural pattern recognition in time-series data.
Patent
Multivariable process trend display and methods regarding same
TL;DR: In this paper, a graphical user display for providing real-time process information to a user for a continuous multivariable process operable under control of a plurality of process variables which include at least manipulated variables and controlled variables is presented.
Journal ArticleDOI
Challenges in the industrial applications of fault diagnostic systems
TL;DR: It is argued that a hybrid blackboard-based framework utilizing collective problem solving is the most promising approach and the efforts of the ASM consortium in pursuing the implementation of the state-of-the-art technologies at plant sites are described.
References
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Book
Introduction to artificial neural systems
TL;DR: Jacek M. Zurada is a Professor with the Electrical and Computer Engineering Department at the University of Louisville, Kentucky and has published over 350 journal and conference papers in the areas of neural networks, computational intelligence, data mining, image processing and VLSI circuits.
Journal ArticleDOI
Representation of process trends—Part I. A formal representation framework
TL;DR: Despite the simplicity of the representation primitives, it is shown that the representation can provide complete, correct, robust and very compact models for process trend histories.
Process fault detection and diagnosis using neural networks
TL;DR: Network trained on single faults are able to accurately diagnose measurement patterns resulting from multiple faults in a large majority of the cases studied and performance during generalization improves with the extent of training.
Journal ArticleDOI
Process fault detection and diagnosis using neural networks—I. steady-state processes
TL;DR: In this article, an analysis of the learning, recall and generalization characteristics of neural networks for detecting and diagnosing process failures in steady state processes is presented, where the single fault assumption has been relaxed to include multiple causal origins of the symptoms.