TY - JOUR
T1 - Novel algorithm for compression of magnetic flux leakage signal data obtained from wire ropes
AU - Hong-yao, Wang
AU - Jie, Tian
AU - Xin, Lv
AU - Xiao-wei, Li
AU - Guo-ying, Meng
AU - Bilen, Sven
AU - Wu, Xinli
N1 - Funding Information:
The authors would like to thank the National Natural Science Foundation of China (No 51774293), the National Programme on Key Basic Research Project (Programme 973; No 2014CB046300), the Yue Qi Young Scholar Project and the Yue Qi Distinguished Scholar Project from China University of Mining and Technology, Beijing, and Central Business Funds of Colleges (Nos 2014QJ01 and 2015QJ04) for providing the financial support for conducting this research. The authors would also like to thank Professors Gang Hua, Zhao Xu, Huifen Li, Hongsheng Yin and Yonggang Xu for their help during the study, as well as the reviewers, whose useful comments and suggestions have helped to improve the manuscript.
Publisher Copyright:
© 2019 British Institute of Non-Destructive Testing. All Rights Reserved.
PY - 2019/2
Y1 - 2019/2
N2 - The safety inspection of wire ropes is vital to ensure the safety of personnel and equipment. The detection of magnetic flux leakage is one of the most practical methods for evaluating the safety of wire ropes. However, compressing the data for this method has to date proven to be difficult. In this study, the lossless compression approach is adopted for data from defective sections of wire rope and the lossy compression approach is used for data from undamaged sections of wire rope. A lossless compression algorithm is then developed based on analysis of the structure and characteristics of the magnetic flux leakage signal. Here, a predictor, which was designed based on the correlation coefficient characteristics of the magnetic flux leakage signal, facilitates the removal of correlations between data from various sampling points. This reduces the information entropy, thereby enhancing the entropy coding efficiency and, in turn, helping to code the predictive error correctly. The experimental results show that for an information entropy of 4.0343 bits for the original signal, the lossless compression algorithm produces an average code length of 3.3 bits. This constitutes an average bit rate reduction of 0.7343 bits and a large reduction in the average number of bits for each data sample. It was found that the proposed algorithm ensures a high compression ratio. Furthermore, the signal-to-noise ratio of the recovery signal based on the proposed compression algorithm increased by 86%.
AB - The safety inspection of wire ropes is vital to ensure the safety of personnel and equipment. The detection of magnetic flux leakage is one of the most practical methods for evaluating the safety of wire ropes. However, compressing the data for this method has to date proven to be difficult. In this study, the lossless compression approach is adopted for data from defective sections of wire rope and the lossy compression approach is used for data from undamaged sections of wire rope. A lossless compression algorithm is then developed based on analysis of the structure and characteristics of the magnetic flux leakage signal. Here, a predictor, which was designed based on the correlation coefficient characteristics of the magnetic flux leakage signal, facilitates the removal of correlations between data from various sampling points. This reduces the information entropy, thereby enhancing the entropy coding efficiency and, in turn, helping to code the predictive error correctly. The experimental results show that for an information entropy of 4.0343 bits for the original signal, the lossless compression algorithm produces an average code length of 3.3 bits. This constitutes an average bit rate reduction of 0.7343 bits and a large reduction in the average number of bits for each data sample. It was found that the proposed algorithm ensures a high compression ratio. Furthermore, the signal-to-noise ratio of the recovery signal based on the proposed compression algorithm increased by 86%.
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U2 - 10.1784/insi.2019.61.2.76
DO - 10.1784/insi.2019.61.2.76
M3 - Article
AN - SCOPUS:85061996943
VL - 61
SP - 76
EP - 82
JO - Insight: Non-Destructive Testing and Condition Monitoring
JF - Insight: Non-Destructive Testing and Condition Monitoring
SN - 1354-2575
IS - 2
ER -