Model based fault diagnosis of machine tools

Author

Isermann, R. ; Reiss, T. ; Wanke, Peter

Author_Institution

Inst. of Autom. Control, Tech. Univ., Darmstadt, Germany

fYear

1991

fDate

11-13 Dec 1991

Firstpage

2574

Abstract

A methodology for the fault diagnosis of machine tools by using a few robust sensors, dynamic process models, and parameter estimation, is described. Changes of process parameters are then symptoms, which are fed into a knowledge-based fault diagnosis component. Then, analytical and heuristic knowledge is treated via fault trees and plausibility measures. Some experimental results with a flexible machining center are given

Keywords

diagnostic expert systems; failure analysis; heuristic programming; machine tools; mechanical engineering computing; parameter estimation; analytical knowledge; dynamic process models; fault trees; flexible machining center; heuristic knowledge; knowledge-based fault diagnosis component; machine tools; model-based fault diagnosis; parameter estimation; plausibility measures; robust sensors; symptoms; Automation; Diagnostic expert systems; Fault detection; Fault diagnosis; Fault trees; History; Knowledge engineering; Machine tools; Milling machines; Parameter estimation; Robustness; Sensor phenomena and characterization; Statistical analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1991., Proceedings of the 30th IEEE Conference on

Conference_Location

Brighton

Print_ISBN

0-7803-0450-0

Type

conf

DOI

10.1109/CDC.1991.261816

Filename

261816