Feature lossless compression method of magnetic flux leakage in-line inspection data based on the combined filtering

In order to solve the problem that the massive data of Ultra-High-Definition(UHD) Magnetic Flux Leakage(MFL) in-line inspection of pipeline is not suitable for storage and analysis, a feature lossless compression method of UHD MFL inline inspection data based on the combined filtering was proposed. According to the signal characteristics, the MFL in-line inspection data was divided into three types, i. e. normal data, small feature data and large feature data, and appropriate filtering algorithms were designed for different types of data, so as to realize the combined filtering of UHD MFL in-line inspection data. Specifically, the normal data were compressed using the Spanning Door Algorithm with high compression ratio, while the small feature data and the large feature data were compressed using the Lempel Zip4(LZ4)lossless compression algorithm with high speed. The results show that the proposed method could smooth the normal data while improve the compression ratio in comparison with the ordinary segmental compression, as well as enhance the signal characteristics of the feature data, which could provide a reference for the compression of UHD MFL in-line inspection data.