NDLI: Fault diagnosis of PTA oxidation process based on decision tree and ant colony optimization

Content Provider	IEEE Xplore Digital Library
Author	Xiaoxia Zheng Feng Qian
Copyright Year	2008
Description	Author affiliation: Sch. of Electr. Power & Autom. Eng., Shanghai Univ. of Electr. Power, Shanghai (Xiaoxia Zheng)
Abstract	Data mining technique can extract desired knowledge from existing databases and ease knowledge acquisition bottleneck of fault diagnosis. A new knowledge acquisition method combined of decision tree and rough set theory is thus proposed for fault diagnosis in this paper. Based on the reduction by rough set theory, decision tree extract diagnostic knowledge from the reduced decision tables in the form of symbolic trees. Trend correlation degree, as the heuristic knowledge, is proposed to evaluate the significance of condition attributes for the construction of decision tree model. Also a modified ant colony algorithm is introduced to determine the optimal fault test sequence of the decision tree model. The proposed method is applied to the fault diagnosis of purified terephthalic acid (PTA) oxidation reactor, which is the key unit in AMOCO PTA production technique. The results show the method is satisfactory and suitable for industrial application.
Starting Page	500
Ending Page	503
File Size	312203
Page Count	4
File Format	PDF
ISBN	9781424417339
DOI	10.1109/CCDC.2008.4597361
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-02
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Rough set theory Correlation Current transformers Trend correlation degree Approximation methods Decision trees Circuit faults Decision tree Ant colony algorithm Information systems Fault currents
Content Type	Text
Resource Type	Article

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