Abstract
The early detection and localization of faults in machines and drives is of primary interest to make further improvements of the reliability and safety. The conventional way is to monitor some important variables like temperatures, pressures, vibrations and to generate alarms if certain limits are exceeded. However, developing internal process faults are only detected at a rather late stage. In recent years research has shown that many process faults can be detected earlier and localized better by using static and dynamic models together with some definite robust sensors. For machines and drives many faults appear as process and signal parameter changes.The contribution describes a general methodology for the fault diagnosis of machines and drives by using few sensors, dynamic process and signal models and parameter estimation. Changes of process and signal 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. The results were obtained in several research projects with machine tools and robots driven by d. c., a.c. and synchroneous motors. Examples show some experimental results.
Published Version
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