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  • Neural-network modeling of a spinning-rotor-gauge zero correction under varying ambient temperature conditions
    Šetina, Janez ; Belič, Igor, 1960-
    Ambient temperature has a significant influence on the zero stability of a spinning rotor gauge (SRG). The sensor element of a SRG is a magnetically levitated steel rotor. The variations of the SRG's ... zero correction depend directly on the variations of the rotor's temperature. The temperature changes of the surrounding components are transferred in high vacuum to the rotor by thermal radiation only. Unfortunately, a direct measurement of the rotor's temperature is not possible, but it is possible to measure the ambient temperature in the vicinity of the SRG suspension head and use these data to predict the temperature-induced variations of the zero corrections. The aim of the presented work is to test the ability of neural networks to model typical daily variations of laboratory temperature and the corresponding SRG zero variations. Three separate feed-forward neural networks are used to form the model. The first neural network models the temperature-time profile, which is needed to adequately smooth the measured data in order to calculate the time derivative of the temperature. The second neural network produces the pressure-time characteristic for the observed time interval. The third neural network combines the time derivative of the temperature and the SRG pressure readout in order to produce the correction for the SRG zero indication.
    Vir: Programme and book of abstracts (Str. 99-100)
    Vrsta gradiva - prispevek na konferenci
    Leto - 2006
    Jezik - angleški
    COBISS.SI-ID - 569770

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