油气管道缺陷漏磁检测数据压缩算法研究

油气管道缺陷漏磁检测所获取的原始数据量很大,而检测器的数据存储容量和处理速度均有限,因此有必要研究快速、高效的缺陷漏磁检测数据压缩算法。文中分析了油气管道缺陷漏磁检测对数据压缩的特殊要求,针对所获得检测数据的特征,提出了一种分段自适应压缩算法。该算法对油气管道缺陷漏磁检测数据进行分段划分并加以识别,针对不同类别的数据段采用不同的压缩方法,且通过改变数据分段的长度和压缩阈值,可调整检测数据的压缩比和压缩失真度。对试验用管道上人工缺陷的漏磁检测数据进行了压缩,结果显示,该算法可在保持较低数据失真度的同时,获得足够高的数据压缩比;压缩效率、压缩质量和复杂度等,均可满足油气管道缺陷漏磁检测对大量数据进行快速、高效压缩的需求。

Research on compression algorithm for MFL detection data of oil and gas pipelines

Considering the massive original data together with limited data storing space and processing speed, it necessary to research fast, efficient and diagnostically lossless compression algorithm in magnetic flux leakage (MFL) detection. This paper analyzes the special requirements of data compression in MFL detection, and proposes a segmented self-adaptive compression algorithm considering the feature of inspection data. The MFL data is divided into different segments, and different compression methods are applied to them. The section length together with compression threshold could adjust the compression ratio and distortion factor. The result of experiment, based on MFL detection data of pipelines with standard defects, shows that the algorithm gets high compression ratio while keeping low distortion factor. The algorithm could meet requirements on efficiency, quality and complexity to compress massive MFL data fast and efficiently.