Analysis Of Differential Pressure Signal Based On Wavelet Transform And Statistics Theory

Gas-liquid two-phase flow exists widely in modern process industry. But the flow pattern identification and the flux measurement remain an international challenge and being studied and explored all around the world. On the basis of self-developed wet gas flowmeter and experiment data, this thesis analyzes measurement data with wavelet and statistics theory. The wavelet theory is used to analyze parameters including multi-scale information entropy, correlation dimension and kolmogorov entropy based on wavelet transform, the singular value of the matrix formed by the multi-dimensional continuous wavelet transform values, the lipschitz exponent. Meanwhile the statistics parameters based on wavelet transform values and higher-order statistics characters are worked out with statistics theory. At last comparisons of different changing relations between these parameters, flow patterns and flux of the two-phase flow are also performed. The analysis of different relations among flow vectors, flow pattern and flux is the key part of this paper. Finally combining of these flow vectors and using them as the inputs of a support vector machine and comparing the performance of a BP network and a support vector machine are performed. The results show that the flow vectors are effective on flow pattern identification and flux prediction. And the performance achieved by a support vector machine is more excellent than that of a BP neuron network system.