We develop a model-plant mismatch (MPM) detection strategy based on a novel closed-loop identification approach and one-class support vector machine (SVM) learning technique. With this scheme we can monitor MPM and noise model change separately, thus separating the MPM from noise model changes. Another advantage of this approach is that it is applicable to routine operating data that may lack any external excitation signals. Theoretical analysis of the proposed closed-loop identification is provided in this paper, showing that it can give a consistent parameter estimate for the process model even in the case where a priori knowledge about the true noise model structure is not available. A set of normal operation data with satisfactory performance is collected as the training data. We build SVM models based on process and noise model estimates from training data to predict the occurrence of MPM in the test data. The proposed technique can be applied to both single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems. Two examples from paper machine control are provided to verify the effectiveness of the proposed MPM detection framework.

我们基于一种新颖的闭环识别方法和一类支持向量机（SVM）学习技术，开发了模型工厂不匹配（MPM）检测策略。通过这种方案，我们可以分别监视MPM和噪声模型的变化，从而将MPM与噪声模型的变化分开。这种方法的另一个优点是，它适用于可能缺少任何外部激励信号的常规运行数据。本文提供了对所提出的闭环辨识的理论分析，表明即使在先验的情况下，它也可以为过程模型提供一致的参数估计。没有关于真实噪声模型结构的知识。收集一组性能令人满意的正常运行数据作为训练数据。我们基于训练数据的过程和噪声模型估计来构建SVM模型，以预测测试数据中MPM的发生。所提出的技术可以应用于单输入单输出（SISO）和多输入多输出（MIMO）系统。提供了两个来自造纸机控制的示例，以验证所提出的MPM检测框架的有效性。