Title :
Model based fault diagnosis of machine tools
Author :
Isermann, R. ; Reiss, T. ; Wanke, Peter
Author_Institution :
Inst. of Autom. Control, Tech. Univ., Darmstadt, Germany
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;
Conference_Titel :
Decision and Control, 1991., Proceedings of the 30th IEEE Conference on
Conference_Location :
Brighton
Print_ISBN :
0-7803-0450-0
DOI :
10.1109/CDC.1991.261816
