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首页> 外文期刊>IEEE Transactions on Industrial Electronics >Slow-Feature-Analysis-Based Batch Process Monitoring With Comprehensive Interpretation of Operation Condition Deviation and Dynamic Anomaly
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Slow-Feature-Analysis-Based Batch Process Monitoring With Comprehensive Interpretation of Operation Condition Deviation and Dynamic Anomaly

机译：基于慢特征分析的批处理过程监控，全面解释操作条件偏差和动态异常

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摘要

In order to provide more sensitive monitoring results, the time dynamics and steady-state operating conditions should be separately monitored by distinguishing time information from the steady-state counterpart. However, it is a more challenging task for batch processes because they vary from phase to phase presenting multiple steady states and complex dynamic characteristics. To address the above issue, a concurrent monitoring strategy of multiphase steady states and process dynamics is developed for batch processes in this paper. On one hand, multiple local models are constructed to identify a steady derivation from the normal operating condition for different phases. On the other hand, based on the recognition that the process dynamics can be considered to be irrelevant with the steady states, a global model is built to detect the dynamics anomalies by monitoring the time variations. Corresponding to alarms issued by different statistics, different operating statuses are indicated with meaningful physical interpretation and deep process understanding. To illustrate the feasibility and efficacy, the proposed algorithm is applied to the injection molding process, which is a typical multiphase batch process.
机译：为了提供更敏感的监视结果，应通过将时间信息与稳态对应物区分开来分别监视时间动态和稳态运行条件。但是，对于批处理而言，这是一项更具挑战性的任务，因为它们在不同阶段之间呈现出不同的稳态和复杂的动态特性。为了解决上述问题，本文针对批处理过程开发了多相稳态和过程动力学的并发监视策略。一方面，构建了多个局部模型以识别不同阶段从正常运行条件得出的稳定推导。另一方面，基于认识到过程动力学可以被认为与稳态无关，因此建立了一个全局模型来通过监视时间变化来检测动力学异常。对应于不同统计信息发出的警报，通过有意义的物理解释和深刻的过程理解来指示不同的操作状态。为了说明可行性和有效性，将所提出的算法应用于典型的多相分批工艺注塑工艺中。

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