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Estimation of uniform static regression model with abruptly varying parameters

机译：参数突变的统一静态回归模型的估计

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A modular framework for monitoring complex systems contains blocks that evaluate condition of single signals, typically of sensors. The signals are modelled and their values must be found within the prescribed bounds. However, an abrupt change of the signal increases the estimated parameters' variance, which raises uncertainty of the sensor condition although it operates correctly. This increase affects the whole system in evaluation of condition uncertainty. The solution must be fast and simple, because of runtime application requirements. The signal is modelled by a static model with uniform noise, variance increase is tested and if detected, the model memory is cleared. The fast and efficient algorithm is demonstrated on industrial rolling data. The method prevents the parameters' variance from the artificial increase.
机译：用于监视复杂系统的模块化框架包含评估单个信号（通常是传感器）状况的模块。对信号进行建模，并且必须在规定的范围内找到它们的值。但是，信号的突然变化会增加估计参数的方差，尽管它可以正确运行，但会增加传感器状态的不确定性。这种增加会影响条件不确定性评估的整个系统。由于运行时应用程序要求，该解决方案必须快速而简单。信号由具有均匀噪声的静态模型建模，测试方差增加，如果检测到，则清除模型存储器。在工业轧制数据上演示了快速有效的算法。该方法防止了人为增加参数的方差。

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