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首页> 外文期刊>Journal of Chemometrics >Fault monitoring based on locally weighted probabilistic kernel partial least square for nonlinear time-varying processes
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Fault monitoring based on locally weighted probabilistic kernel partial least square for nonlinear time-varying processes

机译：基于本地加权概率内核的故障监控用于非线性时变过程的局部加权概率粒子最小二乘

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摘要

In this paper, novel data-driven fault detection and diagnosis approaches are proposed on the basis of a new locally weighted probabilistic kernel partial least squares (LWPKPLS). (a) LWPKPLS can construct an accurate model for a time-varying process by updating itself using the newly coming samples, thus LWPKPLS can be used to monitor time-varying processes. (b) By the integration of local weighted regression and kernel tricks, the LWPKPLS can be applied to construct models for processes with much stronger nonlinear data characteristics. (c) Meanwhile, as a probabilistic regression model, LWPKPLS can process data with random noises and missing values. (d) A set of process monitoring approaches including fault detection and fault diagnosis are developed on the basis of LWPKPLS. At last, the experiment results from a numerical example and an ion-exchange membrane electrolysis process (IEMEP) demonstrate that the proposed process monitoring approaches have satisfactory monitoring performance.
机译：在本文中，基于新的局部加权概率核心最小二乘（LWPKPL）来提出新的数据驱动故障检测和诊断方法。 （a）LWPKPLS可以通过使用新来即将来的样本来更新时变化来构造一个准确的模型，因此LWPKPL可以用于监视时变的过程。 （b）通过集成局部加权回归和内核技巧，可以应用LWPKPLS构建具有更强的非线性数据特性的过程的模型。 （c）同时，作为概率回归模型，LWPKPL可以处理随机噪声和缺失值的数据。 （d）基于LWPKPLS开发了一套包括故障检测和故障诊断的过程监测方法。最后，来自数值例子和离子交换膜电解过程（IEMEP）的实验结果表明，所提出的过程监测方法具有令人满意的监测性能。

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