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A real-time data compression algorithm for gear fault signals

MEASUREMENT 2016年 88卷 165-175页

作者： Han, Shu Liu, Xiaoming Chen, Jia Wu, Jin Ruan, Xiaofei Chongqing Univ Coll Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Ctr Commun & Tracking Telemetering Command Chongqing 400044 Peoples R China

A flexible, stable and controllable real-time algorithm of Auto-Regressive and Moving Average based on Swing Door Trending (ARMA-SDT) is proposed for the compression of impact-type signals in gear fault detection systems. The Auto-Regressive and Moving Average (ARMA) model is used to predict the variation trend of signal features. To guarantee the adaptability, an empirical equation is proposed to calculate the compression threshold of the Swing Door Trending (SDT). Based on the empirical equation and prediction results, dynamic self-regulation of compression threshold is realized, and the compression error always stays around a preconfigured value. Moreover, an experimental setup and an engineering solution are proposed to verify the usefulness, reliability, and stability of the proposed ARMA-SDT algorithm in data compression. (C) 2016 Published by Elsevier Ltd.

关键词： real-time data compression SDT algorithm ARMA predict Signal characteristic

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MiCA: real-time Mixed compression Scheme for Large-Scale Distributed Monitoring 43

MiCA: Real-time Mixed Compression Scheme for Large-Scale Dis...

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43rd Annual International Conference on Parallel Processing (ICPP)

作者： Wang, Bo Song, Ying Sun, Yuzhong Liu, Jun Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing 100190 Peoples R China

ISBN: (纸本)9781479956180

real-time monitoring, providing the real-time status information of servers, is indispensable for the management of distributed systems, e.g. failure detection and resource scheduling. The scalability of fine-grained monitoring faces more and more severe challenges with scaling up distributed systems. The real-time compression which suppresses remote information update to reduce continuous monitoring cost is a promising approach to address the scalability problem. In this paper, we present the Linear compression Algorithm (LCA) which is the application of the linear filter to real-time monitoring. To our best knowledge, existing work and LCA only explores the correlations of values of each single metric at various times. We present a novel lightweight real-time compression Algorithm (ReCA) which employs discovery methods of the correlation among metrics to suppress remote information update in distributed monitoring. The compression algorithms mentioned above have limited compression power because they only explore either the correlations of values of each single metric at various times or that among metrics. Therefore, we propose the Mixed compression Algorithm (MiCA) which explores both of the correlations to achieve higher compression ratio. We implement our algorithms and an existing compression algorithm denoted by CCA in a distributed monitoring system Ganglia and conduct extensive experiments. The experimental results show that LCA and ReCA have comparable compression ratios with CCA, that MiCA achieves up to 38.2%, 27% and 44.5% higher compression ratios than CCA, LCA and ReCA with negligible overhead, respectively, and that LCA, and ReCA can both increase the scalability of Ganglia about 1.5 times and MiCA can increase about 2.33 times under a mixed-load circumstance.

关键词： distributed system monitoring real-time data compression

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Double compression Algorithm with Linear Interpolation of Dynamic Threshold

Double Compression Algorithm with Linear Interpolation of Dy...

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International Conference on Automation, Communication, Architectonics and Materials

作者： Tang, Shi Xi Tang, Ke Ming YanCheng Teachers Univ Informat Sci & Technol Coll YanCheng Peoples R China

ISBN: (纸本)9783037851036

To solve the problem that the duration is too long when the change of the measuring process data of industrial real-time database appears slow, the double compression algorithm with linear interpolation of dynamic threshold is advanced. Good compression quality and reduction effect of process data is accessed. The compression and fitting times are less than 1 ms, and the compression ratio is 6:1 on the 6000 real-time data compression. The compression time is short, and the compression ratio is high. The fitting error between each fitted value and the original value is within the threshold 1.0. The comprehensive performance is better than SLIM and Swinging Door compression algorithm.

关键词： process data compression linear interpolation real-time data compression dynamic threshold

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