Fluid Power Component and Sensor Diagnostics Using an Expert System Shell

Abstract Computer simulation is a powerful and generally accepted practice to carry out research in the area of fluid power. However, accurate parameterization of the component models is required to achieve correct simulation results. The parameters describing the stationary behavior of hydraulic valves are easily available from valve manufacturers’ catalogues. The dynamics are presented typically for the servo valves only. The dynamics of pressure valves are usually more or less unknown even for the manufacturers. Numerical values for simulation purposes are very rarely available. The lack of information concerning valve dynamics makes the component measurements unavoidable. This is time consuming and costly and the benefits of simulation concept in general are reduced. In this paper a method for defining first order dynamics for the pressure compensator is presented. This method can be used in time-domain simulation of fluid power components and systems. The method is based on the analytical dimensions of valve such as diameter of damping orifice, spring constant and mass. Pressure compensated mobile valve is measured for method verification. The presented method can be applied to any type of commercially available well damped single-stage pressure valves. It makes the fluid power component parameterization considerably easier and thereby the advantages reached by simulation are increased.

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Treatment of Uncertainty in an Oncology Protocol by Probabilistic and Artificial Intelligence Approaches

Methods of Information in Medicine ◽

10.1055/s-0038-1635517 ◽

1988 ◽

Vol 27 (01) ◽

pp. 23-33 ◽

Cited By ~ 2

Author(s):

Fiorella de Rosis ◽

G. Steve ◽

C. Biagini ◽

R. Maurizi-Enrici

Keyword(s):

Artificial Intelligence ◽

Sensitivity Analysis ◽

Expert System ◽

Probabilistic Model ◽

Decision Process ◽

Clinical Decision ◽

Final Diagnosis ◽

Decision Models ◽

Expert System Shell ◽

Subjective Estimation

SummaryThe decision process for diagnosis and treatment of Hodgkin’s disease at the Institute of Radiology of Rome has been modelled integrating the guidelines of a protocol with uncertainty aspects. Two models have been built, using a PROSPECTOR-like Expert System shell for microcomputers: the first of them treats the uncertainty by the inferential engine of the shell, the second is a probabilistic model. The decisions suggested in a group of simulated and real cases by a section of the two models have been compared with an “objective” final diagnosis; this analysis showed that, in some cases, the two models give different suggestions and that “approximations” of the shell’s inferential engine may induce wrong conclusions. A sensitivity analysis of the probabilistic model showed that the outputs are greatly influenced by variations of parameters, whose subjective estimation appears to be especially difficult. This experience gives the opportunity to consider the risks of building clinical decision models based on Expert System shells, if the assumptions and approximations hidden in the shell have not been previously analyzed in a careful and critical way.

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