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Application research of multivariate linkage fluctuation analysis on condition evaluation in process industry

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Abstract

Abnormal conditions are hazardous in complex process systems, and the aim of condition recognition is to detect abnormal conditions and thus avoid severe accidents. The relationship of linkage fluctuation between monitoring variables can characterize the operation state of the system. In this study, we present a straightforward and fast computational method, the multivariable linkage coarse graining (MLCG) algorithm, which converts the linkage fluctuation relationship of multivariate time series into a directed and weighted complex network. The directed and weighted complex network thus constructed inherits several properties of the series in its structure. Thereby, periodic series convert into regular networks, and random series convert into random networks. Moreover, chaotic time series convert into scale-free networks. It demonstrates that the MLCG algorithm permits us to distinguish, identify, and describe in detail various time series. Finally, we apply the MLCG algorithm to practical observations series, the monitoring time series from a compressor unit, and identify its dynamic characteristics. Empirical results demonstrate that the MLCG algorithm is suitable for analyzing the multivariable linkage fluctuation relationship in complex electromechanical system. This method can be used to detect specific or abnormal operation condition, which is relevant to condition identification and information quality control of complex electromechanical system in the process industry.

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Authors and Affiliations

  1. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    JunTai Xie, JianMin Gao, ZhiYong Gao, RongXi Wang & Zhen Wang

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  1. JunTai Xie
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  2. JianMin Gao
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  3. ZhiYong Gao
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  4. RongXi Wang
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  5. Zhen Wang
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Correspondence to ZhiYong Gao.

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Xie, J., Gao, J., Gao, Z. et al. Application research of multivariate linkage fluctuation analysis on condition evaluation in process industry. Sci. China Technol. Sci. 61, 397–407 (2018). https://doi.org/10.1007/s11431-017-9138-3

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  • DOI: https://doi.org/10.1007/s11431-017-9138-3

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