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首页> 外文期刊>International Journal of Innovative Computing Information and Control >A FAULT DETECTION SYSTEM FOR AN AUTOCORRELATED PROCESS USING SPC/EPC/ANN AND SPC/EPC/SVM SCHEMES
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A FAULT DETECTION SYSTEM FOR AN AUTOCORRELATED PROCESS USING SPC/EPC/ANN AND SPC/EPC/SVM SCHEMES

机译：使用SPC / EPC / ANN和SPC / EPC / SVM方案进行自相关过程的故障检测系统

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摘要

The statistical process control (SPC) chart is effective in detecting process faults. One important assumption for using the traditional SPC charts requires that the plotted observations are independent to each other. However, the assumption of independent observations is not typically applicable in practice. When the observations are autocorrelated, the false alarms are increased, and these improper signals can result in a misinterpretation. Therefore, the use of engineering process control (EPC) has been proposed to overcome this difficulty. Although EPC is able to compensate for the effects of faults, it decreases the monitoring capability of SPC. This study proposes the combination of SPC, EPC and artificial neural network (SPC/EPC/ANN) and SPC, EPC and support vector machine (SPC/EPC/SVM) mechanisms to solve this problem. Using the proposed schemes, this study introduces a useful technique to detect the starting time of process faults based on the execution of the binomial random experiments. The effectiveness and the beneficial results of the proposed schemes are demonstrated through the use of series simulations.
机译：统计过程控制（SPC）图可有效检测过程故障。使用传统SPC图表的一个重要假设要求绘制的观测值彼此独立。但是，独立观察的假设通常在实践中不适用。当观测值是自相关的时，错误警报会增加，这些不正确的信号可能会导致误解。因此，已提出使用工程过程控制（EPC）来克服此困难。尽管EPC能够补偿故障的影响，但会降低SPC的监视能力。本研究提出将SPC，EPC和人工神经网络（SPC / EPC / ANN）以及SPC，EPC和支持向量机（SPC / EPC / SVM）机制相结合来解决此问题。使用提出的方案，本研究引入了一种有用的技术，该技术基于二项式随机实验的执行来检测过程故障的开始时间。通过使用串联仿真证明了所提方案的有效性和有益结果。

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