Search

Abstract Modelling of received ultrasonic signals can provide basis for both signal analysis and process in various instrumentations based on ultrasonic measurement. At present, some empirical models where the resonance frequencies set to fixed values have been proposed for this purpose. However, significant errors can be found between the existing models and actual ultrasonic signals. This paper proposes a modelling method of received ultrasonic signal based on variable frequency analysis, which is then validated by application on ultrasonic gas flowmeter. In this method, the traditional exponential model is modified to contain a time-varying frequency parameter, whose value can be estimated by analyzing the sampled signal using the Teager Energy Operator (TEO). Other parameters in the model can be further determined by parameter estimation algorithms. In order to meet TEO's application conditions, a sinusoidal transformation of the received signal using Hilbert transform before the time-varying frequency estimation is proposed. Wavelet packet de-noising method, lag parameter and smoothing spline are introduced to improve the performance of the time-varying frequency acquisition in the presence of noise. A number of numerical tests are conducted on simulated signals with different time-varying frequencies and noise to confirm the effectiveness of the proposed method. The actual application significance is validated by analysis of the actual received signals on ultrasonic gas flowmeter. Highlights • Modelling ultrasonic signal with time-varying frequency. • Time-varying frequency estimated by Teager Energy Operator. • Sinusoidal transformation of signal to improve frequency estimation precision. • Possible zero-drift compensation and standard wave gain applications. [ABSTRACT FROM AUTHOR]

Accurate determination of time-of-flight (TOF) is crucially important for precise ultrasonic flow measurement. Detection of ultrasonic signal onset (USO) is considered as an effective approach to determine the actual value of TOF. The USO can be estimated by signal fitting methods. However, the estimation accuracy and reliability of existing methods still need to be improved. This paper proposes a signal fitting method based on artificial fish swarm algorithm and particle swarm optimization combined algorithm (AFSA-PSO). In the method, AFSA is introduced to search all possible solution spaces firstly, considering the multi-modal characteristic of the objective function in signal fitting which is easily being amplified by the strong noise. Then, a feasible solution extraction strategy is proposed to extract the local optimal solution in every space. Finally, PSO is employed to further process the local solutions to obtain the accurate USO. The method is validated by both numerical and experiment tests, using simulated signals with different strength noise and measured signal in actual ultrasonic flowmeter respectively. Comparisons with the methods proposed by other researchers are also given in the paper. The proposed AFSA-PSO is found to be more accurate, more robust, having better anti-noise ability and less time-consuming under a given accuracy requirement. [ABSTRACT FROM AUTHOR]