In practical process industries, a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes, which indicates that the measurements coming from different sources are collected at different sampling rates. To build a complete process monitoring strategy, all these multi-rate measurements should be considered for data-based modeling and monitoring. In this paper, a novel kernel multi-rate probabilistic principal component analysis (K-MPPCA) model is proposed to extract the nonlinear correlations among different sampling rates. In the proposed model, the model parameters are calibrated using the kernel trick and the expectation-maximum (EM) algorithm. Also, the corresponding fault detection methods based on the nonlinear features are developed. Finally, a simulated nonlinear case and an actual pre-decarburization unit in the ammonia synthesis process are tested to demonstrate the efficiency of the proposed method.

中文翻译：

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非线性过程中故障检测的多速率概率主成分分析的核泛化

在实际过程工业中，各种在线和离线传感器和测量仪器已用于过程控制和监测目的，这表明来自不同来源的测量值以不同的采样率收集。为了构建完整的过程监控策略，所有这些多速率测量都应考虑用于基于数据的建模和监控。在本文中，提出了一种新的核多速率概率主成分分析（K-MPPCA）模型来提取不同采样率之间的非线性相关性。在所提出的模型中，使用内核技巧和期望最大值（EM）算法校准模型参数。此外，还开发了相应的基于非线性特征的故障检测方法。最后，