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首页> 外文会议>2011 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering >Condition prediction of chemical complex systems based on Multifractal and Mahalanobis-Taguchi system
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Condition prediction of chemical complex systems based on Multifractal and Mahalanobis-Taguchi system

机译：基于多重分形和Mahalanobis-Taguchi系统的化学复杂系统状态预测

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摘要

Abnormal condition is hazardous which may lead to accidents in chemical industry and effective condition prediction methods are imperative for chemical complex system. Comparing with traditional techniques of condition prediction without concerning nonlinearity of complex system, multifractal analysis elaborately reveals scale-invariance or self-similarity properties of observed data, which is one of the intrinsic characteristics of complex system. To predict the condition of chemical complex system, nonlinear features are extracted from the monitoring variable through multifractal analysis by using Multifractal Detrended Fluctuation Analysis (MF-DFA) algorithm, and multiple variables are investigated through Mahalanobis-Taguchi system (MTS) as a multidimensional analysis method to discover significant patterns. The effectiveness of the approach is illustrated using both experiment data and real data collected from chemical industry
机译：异常状态是危险的，可能导致化学工业事故，而有效的状态预测方法对于化学复杂系统是必不可少的。与不考虑复杂系统非线性的传统状态预测技术相比，多重分形分析精细地揭示了观测数据的尺度不变性或自相似性，这是复杂系统的固有特征之一。为了预测化学复杂系统的条件，使用多分形趋势波动分析（MF-DFA）算法通过多分形分析从监视变量中提取非线性特征，并通过马哈拉诺比斯-田口系统（MTS）作为多维分析来研究多个变量。发现重要模式的方法。使用实验数据和从化工行业收集的真实数据说明了该方法的有效性

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