As a core part of modern chemical plants, key performance indicator oriented process monitoring and fault diagnosis systems have gradually made great contributions to guaranteeing process safety, improving product quality, and ensuring system reliability, which recently have received extensive attention and become one of the hot spots both in academic research and industrial application fields. Different from previous methods, a novel key performance indicator oriented process monitoring method is proposed in this paper, which fully mines and utilizes important time feature information hidden in the process data while considering the local process information. Firstly, a group of representative process variables with maximum key performance indicator information are selected by the maximal information coefficient algorithm, and local information is extracted. Then, observed value, accumulated error, and change rate information are further extracted from the representative process variables and expanded into multiple information blocks, which contain both local process and hidden time feature information. After that, the support vector data description model is established to monitor each information block, and the Bayesian inference is employed to fuse the final monitoring results to obtain a new monitoring index. Finally, the performance and effectiveness of the proposed method is validated by conducting a simulation on Tennessee Eastman process.

中文翻译：

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基于多信息提取和支持向量数据描述的面向关键性能指标的过程监控新方法

面向关键性能指标的过程监控与故障诊断系统作为现代化工厂的核心部分，在保障过程安全、提高产品质量、保障系统可靠性等方面逐渐做出了巨大贡献，近年来受到广泛关注，成为热点之一。在学术研究和工业应用领域都有亮点。与以往方法不同，本文提出了一种新的面向关键性能指标的过程监控方法，在考虑局部过程信息的同时，充分挖掘和利用隐藏在过程数据中的重要时间特征信息。首先，通过最大信息系数算法选择一组具有最大关键性能指标信息的具有代表性的过程变量，并提取本地信息。然后，进一步从代表过程变量中提取观测值、累积误差和变化率信息，并扩展为多个信息块，其中包含局部过程和隐藏时间特征信息。之后，建立支持向量数据描述模型对每个信息块进行监测，并利用贝叶斯推理对最终监测结果进行融合，得到新的监测指标。最后，通过对田纳西伊士曼过程进行模拟，验证了所提方法的性能和有效性。其中包含本地进程和隐藏时间特征信息。之后，建立支持向量数据描述模型对每个信息块进行监测，并利用贝叶斯推理对最终监测结果进行融合，得到新的监测指标。最后，通过对田纳西伊士曼过程进行模拟，验证了所提方法的性能和有效性。其中包含本地进程和隐藏时间特征信息。之后，建立支持向量数据描述模型对每个信息块进行监测，并利用贝叶斯推理对最终监测结果进行融合，得到新的监测指标。最后，通过对田纳西伊士曼过程进行模拟，验证了所提方法的性能和有效性。