This paper reviews our recent work on vibrating sensors for the physical properties of fluids, particularly viscosity and density. Several device designs and the associated properties, specifically with respect to the sensed rheological domain and the onset of non-Newtonian behavior, are discussed.

For many applications, resonating sensors can be designed which exhibit excellent sensitivity of the resonant parameters (resonant frequency and quality factor) to the wanted physical parameters. When the resonant parameters are to be derived from measured data, the utilized signal processing algorithm significantly affects the precision of the obtained results, in particular, when the resonance is impaired with spurious contributions. In particular, for systems with low Q-factors (e.g., electromechanical resonators in viscous liquids with Q < 100), the measurement precision suffers from various unwanted spectral components induced by parasitic effects of the resonator and measurement errors. In order to separate the adverse effects from the ideal second-order characteristics, three related methods for estimating the parameters of second-order resonant systems are introduced in this paper extending established methods. To this end, the spectrum is separated into a component induced by a second-order resonant system and an unknown background spectrum.

No abstract

No abstract

A recently introduced method for robust determination of the parameters of strongly damped resonances is evaluated in terms of achievable accuracy. The method extracts and analyzes the locus of the resonant subsystem of noisy recorded complex spectra, such that the interfering influences of many environmental factors are eliminated. Estimator performance is compared to the absolute lower limit determining the Cramér–Rao lower bound (CRLB) for the variance of the estimated parameters. A generic model that is suitable for representation of a large class of sensors is used and analyzed. It is shown that the proposed robust method converges to the CRLB for low measurement noise.