Fault detection method based on LECA for multimode process

doi:10.11949/j.issn.0438-1157.20150441

Abstract

Abstract:

Aiming at the multimode process data which follow complex distribution in industrial process monitoring, this paper proposes a fault detection method based on local entropy component analysis (LECA) algorithm. In order to deal with the multimode characteristic of operating data, k nearest neighbor Parzen window (KNN-Parzen) method is used to estimate the local probability density of each sample. Then, a local relative density estimate function is constructed to decrease the sensitivity to window width parameter. To effectively extract the feature information hidden in the non-Gaussian data, the local entropies of process data are calculated by using information entropy theory. Independent component analysis (ICA) is applied to establish the local entropy component statistic model for fault detection. The simulation results of numerical example and continuous stirred tank reactor (CSTR) system indicate that LECA can show superior performance in process monitoring.

Key words: fault detection, multimode process, local relative density estimate, information entropy, independent component analysis

摘要：

针对工业过程监控中的多工况复杂分布数据,提出一种基于局部熵成分分析(LECA)的故障检测方法。为处理数据的多模态分布问题,LECA首先采用KNN-Parzen窗方法估计变量的局部概率密度,进一步构造局部相对概率密度函数降低对窗参数选择的敏感性。为有效挖掘非高斯分布数据中的特征信息,利用信息熵理论计算过程数据的局部信息熵,并采用独立元分析(ICA)方法建立局部熵成分统计模型,实时检测过程故障。在数值例子和连续搅拌反应釜(CSTR)上的仿真结果表明,该方法在故障检测过程中能够获得较好的监控性能。

关键词: 故障检测, 多工况过程, 局部相对概率密度估计, 信息熵, 独立元分析算法

CLC Number:

TP277

ZHONG Na, DENG Xiaogang, XU Ying. Fault detection method based on LECA for multimode process[J]. CIESC Journal, 2015, 66(12): 4929-4940.

钟娜, 邓晓刚, 徐莹. 基于LECA的多工况过程故障检测方法[J]. 化工学报, 2015, 66(12): 4929-4940.

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