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multivariate statistical monitoring of multiphase batch processes with between-Phase Transitions and Uneven operation durations

机译:具有相间过渡和运行时间不均匀的多阶段批处理过程的多变量统计监视

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摘要

In order to achieve satisfactory monitoring, multivariate statistical process models should well reflect process nature. In manufacturing systems, many batch processes are inherently multiphase. Usually, different phases have different characteristics, while gradual transitions are often observed between phases. Another important feature of batch processes is the unevenness of operation durations. Especially, in multiphase batch processes, the situation becomes more complicated. In this study, a batch process modelling and monitoring strategy is proposed based on Gaussian mixture model (GMM), which can automatically extract phase and transition information for uneven-duration batch processes. The application results verify the effectiveness of the proposed method.
机译:为了实现令人满意的监视,多元统计过程模型应很好地反映过程的性质。在制造系统中,许多批处理过程本质上都是多阶段的。通常,不同的阶段具有不同的特性,而通常会在阶段之间观察到逐渐过渡。批处理的另一个重要特征是操作时间的不均匀性。尤其是在多阶段分批处理中,情况变得更加复杂。在这项研究中,提出了一种基于高斯混合模型(GMM)的批处理过程建模和监控策略,该策略可以自动提取不均匀持续时间的批处理过程的相和过渡信息。应用结果验证了该方法的有效性。

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