Research On Classification Compression Method Of Pipeline Magnetic Flux Leakage Data

With the development of national economy, the pipeline is playing an irreplaceable role for its unique advantage in the long-distance oil transportation. However, long-time running makes the aging and corrosion phenomenon in the pipeline, which not only leads to frequent leakage accidents, but also causes the significant economic loss, environment pollution and security risk. It makes sense in state property and natural environment protection to check out the pipeline leakage rapidly and accurately.Pipeline intelligence MFL detector uses pipeline conveyance medium for power to do the online nondestructive detection on the pipeline directly, which is recognized as the perfect pipeline detection method at home and abroad now. However, when the detection is working, large amount of the data obviously troubles the storage and processing of real-time online signals. The flux leakage data is so valuable that the data compression is imperative. Under the background of the863Project-"MFL detection of submarine pipeline", according to the above problem, the following work will be done:Firstly, the character of MFL detection data is studied. MFL original data consists of the health data, defect data and non-defect data (noise). First difference method and dynamic range threshold method are utilized for significant range segmentation of the original data so as to realize the intelligent data classification method.Secondly, the basic theory of data compression is studied and the characters of each compression algorithm are compared. The final compression algorithm is confirmed.Thirdly, the Huffman code is chosen as the lossless compression algorithm for the defect data according to the character of the MFL data, because the Huffman code is good at the instantaneity and simplification in the hardware realization. The superiority of Huffman algorithm is demonstrated. The program in Matlab is compiled for the testing.Finally, LZW (Lempel-Ziv-Welch) coding algorithm is used for the compression of the large amount of the health data. On the basis of the analysis on the shortage of the classic LZW coding, the optimization is raised in the aspects of dictionary storage, matching length encoding and search efficiency. Through the repeated experiments, the summary and analysis are made about each parameter value of the improved algorithm. It’s been demonstrated for the accuracy and feasibility of the improved LZW coding algorithm.