Learning methods for online-process diagnosis

被引：1

作者：

Feucht, P ^{[1
]}

Zoellner, JM ^{[1
]}

Berns, K ^{[1
]}

Zirzlaff, T ^{[1
]}

Leisin, O ^{[1
]}

机构：

[1] Univ Karlsruhe, Forschungszentrum Informat, D-76131 Karlsruhe, Germany

来源：

12TH IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2000年

关键词：

D O I：

10.1109/TAI.2000.889883

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Because of very high workpiece costs in manufacturing processes production errors shall be detected online to avoid a series of defective workpieces. This article describes a qualitative evaluation method for time series that is applied to the diagnosis of spraying procedure parts of car bodies. The determination of the parameters for the procedure is gained through learning data which simplifies the industrial use enormously. An already employed prototype in the production confirms the Expected functionality of the procedure.

引用

页码：281 / 284

页数：4

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