摘要
天然气管道泄漏监测正在进入大数据时代,针对传统方法存在的采集数据冗余、特征提取及识别受主观因素影响较大等问题,结合压缩感知与深度学习理论,提出一种在变换域进行泄漏信号的压缩采集、在压缩感知域进行自适应特征提取及识别的智能天然气管道泄漏孔径识别方法。通过随机高斯矩阵获取压缩采集数据,并通过深度学习挖掘测量信号中隐藏的泄漏孔径信息,经稀疏滤波实现特征的自动筛选,最后研究了softmax回归实现孔径的高精度分类识别。实验结果表明,该方法实现了监测数据的压缩,对压缩感知域采集信号的识别性能明显优于传统方法。
Natural gas pipeline leak monitoring is entering the age of big data. Aiming at the problems of traditional methods, such as redundant data, subjective feature extraction and identification, an intelligent pipeline leak aperture identification method is proposed combined compressed sensing （CS） and deep learning theory, which can achieve compressed sampling, adaptive feature extraction and recognition. The random Gaussian matrix is used to acquire the compressed acquisition data, and the aperture information contained in measured samples in CS domain is analyzed by deep learning. The sparse filtering is applied to realize the automatic feature selection. Finally, the high precision classification and recognition of the aperture is obtained by softmax regression. Experimental results show that this method realizes the compression of the monitoring data, and the identification performance for data of compressed sensing domain is better than traditional methods.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2017年第12期3071-3078,共8页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(51204145)
河北省自然科学基金(E2016203223
E2013203300)项目资助
