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A Condition Monitoring Methodology Based on a Non-Parametric System Identification Approach

机译:基于非参数系统辨识方法的状态监测方法

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The paper presents a non-parametric approach to a general model-based scheme of the condition monitoring of a technology process. Within such an approach, the total inference on a technology process status is implemented by current comparing preliminary nominal indicators obtained, which correspond to the nominal technology process model with corresponding characteristics obtained by current observations. A consistent measure of stochastic dependence of random processes is proposed to be used as a technology process behavior indicator. Such a measure is the maximal correlation function. It consistently captures the actual nonlinear dependence between random processes, while conventional measures of dependence based on the dispersion and, all the more so, ordinary product correlation functions do not. Meanwhile, there are known examples demonstrating that actual dependence between model variables may be nonlinear even if the regression of a variable onto another one is linear. Within the case, such dependence can be properly described by maximal correlation ultimately. Such a consideration justifies applying consistent measures of dependence when applied to the model-based condition monitoring methodology.
机译:本文提出了一种非参数方法,用于基于通用模型的技术过程状态监控方案。在这种方法中,对技术过程状态的总推断是通过当前比较获得的初步名义指标来实现的,这些指标与名义技术过程模型相对应,并具有通过当前观察获得的相应特征。提出了一种随机过程的随机依赖性一致性度量,以用作技术过程行为指标。这种度量是最大相关函数。它始终如一地捕获随机过程之间的实际非线性相关性,而基于色散的常规相关性度量(更重要的是基于常规乘积相关函数)则没有。同时,有一些已知的示例表明,即使变量在另一变量上的回归是线性的,模型变量之间的实际依存关系也可能是非线性的。在这种情况下,最终可以通过最大相关性恰当地描述这种依赖性。当将这种考虑应用于基于模型的状态监视方法时,可以证明采用一致的依赖性度量是合理的。

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