Complex modern industrial processes often exhibit multimodal characteristic because of different manufacturing strategies. Although the conventional auto-associative kernel regression (AAKR) method is suitable for monitoring the nonlinear multimodal processes, different high Pearson correlations between variables from different modes reduce the fault detection accuracy of AAKR model. Moreover, within-mode process data usually present the property of serial correlation, resulting in the auto-correlation and cross-correlation of variables simultaneously existing in the dynamic processes. In order to reduce the influence of correlation and improve the accuracy of fault detection based on AAKR, a novel multimode process monitoring method combining zero-phase component analysis (ZCA) as a data preprocessing technique and traditional AAKR is proposed. To further enhance the detectability of the model to small disturbance, a modified statistic based on multivariate exponentially weighted moving average (MEWMA) is studied in this paper. The monitoring performance of the proposed approach is evaluated through a numerical example, the Tennessee Eastman (TE) process and the real Bisphenol-A (BPA) production process.

中文翻译：

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使用自适应自动关联内核回归的多模式过程监控

由于不同的制造策略，复杂的现代工业过程通常表现出多模态特征。尽管传统的自关联核回归（AAKR）方法适用于监测非线性多模态过程，但不同模态变量之间不同的高 Pearson 相关性降低了 AAKR 模型的故障检测精度。此外，模内过程数据通常具有序列相关性，导致动态过程中同时存在变量的自相关和互相关。为了减少相关性的影响，提高基于AAKR的故障检测精度，提出了一种将零相分量分析(ZCA)作为数据预处理技术与传统AAKR相结合的多模过程监测方法。为了进一步提高模型对小扰动的检测能力，本文研究了一种基于多元指数加权移动平均（MEWMA）的修正统计量。通过数值示例、田纳西伊士曼 (TE) 工艺和实际的双酚 A (BPA) 生产工艺评估了所提出方法的监测性能。