作者：
P. WankeInstitut of Automatic Control
Laboratory of Control Systems and Process Automation Technical University Darmstadt Landgraf-Georg-Str. 4 D-6100 Darmstadt Germany
The early detection and localization of faults in machines and drives is of primary interest to make further improvement of the reliability and saftey. This paper presents a new approach in fault diagnosis and supervi...
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The early detection and localization of faults in machines and drives is of primary interest to make further improvement of the reliability and saftey. This paper presents a new approach in fault diagnosis and supervision of main drives with elastic components. The supervision of drives with additional sensors for temperature, pressures or vibrations is usually expensive. Currently, internal process faults are only detected partially and at a rather late stage, by generating alarms if certain limits of the measured signals are exceeded or limit switches stop the machine tool. A process model based approach for fault diagnosis was developed. A dynamic model of the main drive was derived and together with easily measurable signals of the drive, like current and speed, physical parameters can be estimated. Changes of those process parameters are the symptoms for a diagnostic inference mechanism. Experimental results with least squares parameter estimation in the form of disrete square root filters are shown.
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 paper provides a dynamic analysis of a COGAS Propulsion Plant, including mathematical modeling and simulation, and concludes with the results of a COGAS simulation which indicates encouraging conclusions regarding...
Challenged by urbanization and increasing travel needs, existing transportation systems need new mobility paradigms. In this article, we present the emerging concept of autonomous mobility-on-demand, whereby centrally...
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Challenged by urbanization and increasing travel needs, existing transportation systems need new mobility paradigms. In this article, we present the emerging concept of autonomous mobility-on-demand, whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers. We provide a comprehensive review of methods and tools to model and solve problems related to autonomous mobility-on-demand systems. Specifically, we first identify problem settings for their analysis and control, from both operational and planning perspectives. We then review modeling aspects, including transportation networks, transportation demand, congestion, operational constraints, and interactions with existing infrastructure. Thereafter, we provide a systematic analysis of existing solution methods and performance metrics, highlighting trends and trade-offs. Finally, we present various directions for further research.
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