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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]

To reduce the large integration errors brought by traditional methods in gas flow measurement under complex flow field, artificial neural network, support vector machine and other intelligent algorithms have been put into use. But these intelligent methods consume much time for training and need intensive user intervention for network design. This paper proposes to apply extreme learning machine to multipath ultrasonic flowmeters, which can analytically determine the output weights of networks instead of error backpropagation algorithm and iterative tuning of the parameters, and therefore provide high metering accuracy at extremely fast learning speed as well as require least human intervention. To test its effectiveness under different flow field and sensitivity to complex flow profiles, extreme learning machine is applied to determine the flow rate under two piping configurations, which can produce mild and severe flow disturbances. The determination errors are compared with a traditional integration method on the position of 5D and 10D as well as with the path orientation of 0° and 90°. Then 7 installation angles and 9 installation positions are respectively configured to study the performance and sensitivity of UFMs to installation effects. Finally, a comparison between extreme learning machine and other two intelligent algorithms is made in training and test time, the mean squared error and the maximal metering error under severe flow disturbance. It is found that extreme learning machine has rather high determination accuracy for flow rate at extremely fast learning speed and it is insensitive to the installation effects of ultrasonic gas flowmeter. [ABSTRACT FROM AUTHOR]