In modern manufacturing systems it is very important to increase the safety, reliability and availability of the involved components, e.g. industrial robots. Downtime of the entire production line as a result of a sin...
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In modern manufacturing systems it is very important to increase the safety, reliability and availability of the involved components, e.g. industrial robots. Downtime of the entire production line as a result of a single component breakdown will cause high costs which have to be avoided. Hence automatic controlled systems of high complexity should be supervised by automatic means and supplemented by appropriate diagnosis methods. Continuous time model-based procedures have been proven to present powerful tools for this purpose. In the present contribution specific approaches of parameter estimation and state estimation are applied to the mechanical components of an industrial robot. Several changes which represent typical mechanical faults were established artificially to the robot. Finally the different approaches are compared.
作者：
J. BöhmInstitute of Automatic Control
Laboratory of Control Engineering and Process Automation Technical University Darmstadt Landgraf-Georg-Strasse 4 D-6100 Darmstadt Germany
A model—based method for sensing external forces between a workpiece and the robot’s end-effector from drive current signals will be presented. To estimate these active forces only the natural process signals (armat...
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A model—based method for sensing external forces between a workpiece and the robot’s end-effector from drive current signals will be presented. To estimate these active forces only the natural process signals (armature current and actual joint position) of the robot are used. These signals are usually available in the control system of the robot. So no additional sensor is required. The force signal in the armature current will be interpreted as a disturbing torque which is estimated by a third order adaptive disturbance observer based on mathematical dynamic models for the robot. Finally first experimental results will be presented which were obtained with an industrial robot with six degrees of freedom.
作者：
R. IsermannInstitute of Automatic Control
Laboratory of Control Engineering and Process Automation Technical University Darmstadt Landgraf Georg Straße 4 D-6100 Darmstadt Germany
For further improvement of the reliability and safety of machines the automatic early detection and localization of faults is of high interest. The conventional approach is to monitor some important variables like tem...
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For further improvement of the reliability and safety of machines the automatic early detection and localization of faults is of high interest. The conventional approach is to monitor some important variables like temperatures, pressures, vibrations and to generate alarms if certain limits are exeeded. However, developing internal faults are in this way only detected at a rather late stage. By applying static and dynamic process models and common process input and output measurements the inherent relationships and redundancies can be used to detect faults earlier and to localize them better. Changes in process and signal parameters are very well suited for fault detection. The paper describes a general methodology for machines and other processes by using few measurements, dynamic process and signal models and parameter estimation to generate symptoms. The detected symptoms are fed into a knowledge based fault diagnosis procedure. Analytical and heuristic knowledge is treated via fault-symptom trees, process history and plausibility measures. The considered machines consist of a motor, a drive chain and a working process or load. They may be electrical motor or combustion engine driven pumps, fans or machine tools with gear or belt drive chains. The described methodology was developed and tested experimentally for several machines. As one example, experimental results are shown for a d.c. motor powered feed drive of a machine tool.
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...
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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.
作者：
D. NeumannTechnical University of Darmstadt
Inst. of Control Engineering Laboratory for Automatic Control and Process Automation Landgraf-Georg-Str. 4 D-6100 Darmstadt FRG
The paper describes a personal-computer-based fault diagnosis system, which is designed for the analysis of periodically working machine tools. The used algorithms are described from signal analysis to the diagnosis s...
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The paper describes a personal-computer-based fault diagnosis system, which is designed for the analysis of periodically working machine tools. The used algorithms are described from signal analysis to the diagnosis system including some tests with real machines. The system was synthesized modularly. so that it can easily be adapted to other processes. Only few standard signals are needed. which have to be measured for controlling purposes anyway. so normally no special sensors have to be added. The knowledge based diagnosis system uses furtheron heuristic knowledge from machine history and expert knowledge. which is not exactly quantifiable.
作者：
BULL, DNDaniel N. Bull
Ph.D. is a consultant in fermentation technology and president of Satori Corporation P.O. Box 1730 Montclair N.J. 07042. (201) 783-9787.REFERENCES Graff G.M. Short H. and Keene J.1983. Gene-splicing methods move from lab to plant. Chem. Eng.90: 22-27.|ISI|Broda P.1979. p. 1-3. Plasmids. W. H. Freeman Oxford and San Francisco.Donoghue D.J. and Sharp P.A.1978. Construction of a hybrid bacteriophage-plasmid recombinant DNA vector. J. Bact.136: 1192-1196.|PubMed|ISI|ChemPort|Bok S.H. Hoppe D. Mueller D.C. and Lee S.E.1983. Improving the production of recombinant DNA proteins through fermentation development. Abstract from 186th ACS Natl. Mtg. Washington D.C. Sept. 1.Maniatis T. Fritsch E.F. and Sam-brook J.1982. p. 88. Molecular Cloning. Cold Spring Harbor Laboratory. Guidelines for research involving recombinant DNA molecules June 1983
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A simplified theoretical model for the dynamics of a side-fired steam reformer is derived. Experimental work, designed to confirm the theoretical model, is described, and the use of the results in the design of a feed...
A simplified theoretical model for the dynamics of a side-fired steam reformer is derived. Experimental work, designed to confirm the theoretical model, is described, and the use of the results in the design of a feed-forward/feedback control scheme for throughput changes is discussed. This scheme, implemented on an on-line computer, enables large throughput changes to be made with a minimum of disturbance to the plant and a minimum of effort by the plant operators. Results from a typical throughput change are presented.
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