关键词: 液化天然气, 分层, 涡旋, AR谱分析, 欧式距离, 模式识别

Abstract:

By selecting the temperature as a characterization parameter and the pole parameter of AR model of temperature sequence as the feature parameter of front-end processing, the treatment of temperature signals by an intelligent system is simulated based on the spectral analysis and the time - frequency distribution theory , and then the recognition and diagnosis of stratification and rolling are performed for the liquefied natural gas （LNG） in tank. Moreover, the character-selecting method based on distance criterion is adopted to compress the feature dimension and to select an optimal character with good separability. The results of feature extraction for temperature sequence show that （ 1 ） from the stratification state to the rolling state, the center frequency of the main peak deviates from the increasing peak value ; （2） the fuzzy AR spectrum in the critical stratification and rolling states consists of both characteristic peaks and some other peaks ; and （3） the stratification and rolling states are of different characteristic parameters such as the center frequency, the peak value, the high-frequency energy and the high-frequency energy ratio. Moreover, the results of pattern recognition for stratification and rolling based on the Euclidean distance of AR model parameter indicate that the Euclidean distances in similar states are approximate to each other but are not equal to zero due to the randomness of temperature signals, and that the obvious differences in Euclidean distances in different states help to implement the diagnosis and recognition of stratification and rolling for LNG.

Key words: liquefied natural gas, stratification, rolling, AR spectral analysis, Euclidean distance, pattern recognition