Abstract
In the process of transient test, due to the insufficient bandwidth of the pressure sensor, the test data is inaccurate. Firstly, based on the projection of the shock tube test signal in the sparse domain, the feature expression of the signal sample is obtained. Secondly, the problem of insufficient bandwidth is solved by inverse modeling of sensor dynamic compensation system based on swarm intelligence algorithm. In this paper, the method is used to compensate the shock tube test signals of the 85XX series pressure sensors made by the Endevco company of the United States, the working bandwidth of the sensor is widened obviously, the rise time of the pressure signal can be compensated to 12.5 μs, and the overshoot can be reduced to 8.96%. The repeatability of dynamic compensation is verified for the actual gun muzzle shock wave test data, the results show that the dynamic compensation can effectively recover the important indexes such as overpressure peak value and positive pressure action time, and the original shock wave signal is recovered from the high resonance data.
Highlights
Transient signal refers to the signal with short duration, wide spectrum range, and obvious beginning and end
In the national project participated by the author, the shock wave was measured by 85XX series pressure sensors made by the Endevco company of the United States
The traditional dynamic compensation methods can be divided into two categories: one is to identify the sensor system based on the sensor model, on this basis, compensation links are constructed, such as zero pole assignment method and deconvolution method, etc [4, 7, 8]
Summary
Transient signal refers to the signal with short duration, wide spectrum range, and obvious beginning and end. The dynamic compensation method based on the swarm intelligence algorithm has high precision [1, 3, 5, 6]. This paper proposes to design a sparse domain filter, combined with different swarm intelligence algorithms, aiming at the sparse characteristics of the signal to be compensated, that is, many zero value coefficients are generated after the sparse transformation [9], which reduces the solution parameters in the calculation process, reduces the solution space, obtains higher solution accuracy and faster solution speed, and reduces the complexity of the model.
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