Two-level fusion big data compression and reconstruction framework combining second-generation wavelet and lossless comp
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ORIGINAL ARTICLE
Two-level fusion big data compression and reconstruction framework combining second-generation wavelet and lossless compression Zhang Chuanchao1,2 Received: 10 March 2020 / Accepted: 14 May 2020 © The Author(s) 2020
Abstract In view of the characteristics of big data, fuzziness, and real time of data acquisition and transmission in the fuzzy information system faced by aircraft health management, to reduce the load of airborne data processing and transmission system under the condition of limited airborne computing resources and strong time constraints, the data collected by the airborne system are first compressed, and the amount of data are reduced before transmission and reconstructed after transmission. In view of the situation that the compression ratio of primary data compression is too small and the compression time is too long for large-scale fuzzy systems to meet the transmission requirements of the system, this paper combines the advantages of lossy compression method which consumes less time and lossless compression method which has higher compression ratio, and innovatively proposes a two-level data compression and reconstruction framework combining lossy compression and lossless compression. The optimization analysis is carried out. Taking a real aero-engine health sample as an example, the validity, scientificity, and robustness of the proposed framework are verified by comparing with data compression and reconstruction algorithm based on redundant sparse representation and compressed sensing. Keywords Large-scale fuzzy information system · Data compression and reconstruction · Second-generation wavelet · Lossless compression and reconstruction · Two-level fusion framework · Aircraft health management
Introduction Aircraft health management parameters and data size are multiplying. In the 1980s, the aircraft health monitoring parameters were only 3000, while up to now, the most advanced civil aircraft has 400,000 monitoring parameters. The historical data of airlines operation and maintenance are increasing day by day. The annual operation data of the major airlines have reached tens TBs. These big data monitoring and acquisition have brought a great burden to the limited air-ground data links, but also increased the workload of maintenance engineers and technicians, and reduced the work efficiency. Therefore, in the era of big data of aircraft, it is imperative to study the compression and
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Zhang Chuanchao [email protected]
1
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, Hubei, People’s Republic of China
2
Aviation Industry Corporation of China, Beijing 100028, People’s Republic of China
reconstruction algorithm of large data to enable maintenance personnel to determine the failure cause and maintenance measures in time, and make joint efforts to arrange maintenance time to provide technical support, so as to achieve preventive maintenance, improve aircraft dispatch rate, and reduce maintenance costs. However, aircraft health management da
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