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首页> 外文会议>Chinese Automation Congress >Incipient Fault Detection of Nonlinear Processes Based on Probablility Related SVDD in Local Variable Field
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Incipient Fault Detection of Nonlinear Processes Based on Probablility Related SVDD in Local Variable Field

机译：基于遗忘相关SVDD在局部变量字段中的非线性过程的初始故障检测

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摘要

Support vector data description (SVDD) is an effective algorithm for nonlinear process monitoring. However, the conventional SVDD method can't deal with the incipient faults well, which has the small fault amplitude and is easy to be overlapped by industrial noises. Aiming at this problem, an improved SVDD, called probability related SVDD in local variable field (LVPSVDD), is proposed to detect the incipient faults in nonlinear processes. Firstly, the method divides process variables into several local variable fields by hierarchical clustering, and the SVDD model of each variable field is built. Then the sliding window technology is applied to the SVDD distance statistics, and the probability distribution change in the sliding window is measured by Kullback Leibler divergence (KLD). For each local variable field, the corresponding probability related monitoring statistic is developed to replace the original distance statistic. Finally, the global monitoring statistic is obtained by integrating the monitoring results of all local variable fields with Bayesian inference strategy. The method is illustrated with simulation on the continuous stirred tank reactor (CSTR).
机译：支持向量数据描述（SVDD）是非线性过程监控的有效算法。然而，传统的SVDD方法无法处理初始故障良好，这具有小的故障幅度，并且易于通过工业噪声重叠。针对这个问题，提出了一种改进的SVDD，称为局部可变字段（LVPSVDD）中的概率相关SVDD，以检测非线性过程中的初始故障。首先，该方法通过分层群集将过程变量划分为几个本地变量字段，构建每个变量字段的SVDD模型。然后将滑动窗技术应用于SVDD距离统计，并且通过Kullback Leibler发散（KLD）测量滑动窗口中的概率分布变化。对于每个局部可变字段，开发了相应的概率相关的监视统计以取代原始距离统计信息。最后，通过将所有局部变量字段的监测结果与贝叶斯初推断策略集成来获得全局监测统计。该方法以连续搅拌釜反应器（CSTR）的模拟进行说明。

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