NDLI: Learning behavior models of hybrid systems using wavelets for autonomous jumps detection

Content Provider	IEEE Xplore Digital Library
Author	Vodencarevic, A.
Copyright Year	2012
Description	Author affiliation: Knowledge-Based Systems Research Group, Department of Computer Science, International Graduate School Dynamic Intelligent Systems, University of Paderborn, 33098 Paderborn, Germany (Vodencarevic, A.)
Abstract	Today's hybrid technical systems, such as production facilities, evolve rapidly. Therefore the efforts and resources needed to obtain their trustworthy behavior models are significantly increased. This forms the modeling bottleneck, as the model creation mostly relies on the expensive and durable manual modeling. Behavior models are of the greatest importance in monitoring, anomaly detection, and diagnosis applications. To deal with this issue, the novel HyBUTLA algorithm was recently proposed for automated learning of behavior models from process data. Despite being the first hybrid automaton learning algorithm, it does not model the autonomous jumps (abrupt changes in process variables), and it suffers from long runtime due to the use of advanced machine learning methods. In this paper the HyBUTLA algorithm is improved to account for the autonomous jumps by introducing the splitting step based on discrete wavelet transform. This significantly improves the algorithm runtime, since high function approximation accuracy can be reached by using rather simple methods such as multiple linear regression. The benefits that splitting brings are formally proved and demonstrated in a real-world production system.
Starting Page	151
Ending Page	156
File Size	1193878
Page Count	6
File Format	PDF
ISBN	9781467303125
e-ISBN	9781467303118
DOI	10.1109/INDIN.2012.6301128
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Learning automata Approximation algorithms Discrete wavelet transforms Function approximation
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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