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Data-driven bounded-error fault detection
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DOI：10.1002/acs.2443.png)
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Abstract
En 中文
In this paper, a new data-driven fault-detection method is proposed. This method is based on a new nonparametric system identification approach, which constitutes the principal contribution to this work. The fault-detection method is a parametric model-free approach that can be applied to nonlinear systems that work at various operating points. Not only can the fault-detection process be applied to the steady state of each operating point, but it can also be applied to the transient state resulting from a change in the operating point. In order to detect faults, the proposed method uses an interval predictor based on bounded-error techniques. The utilization of techniques based on bounded error enables system uncertainties to be included in an explicit way. This in turn leads to the possibility of obtaining interval predictions of the behaviour of the system, which include information on the reliability of the prediction itself. In order to show the effectiveness of the fault-detection method, two examples are presented: in the form of a simulated process (counter-flow shell-and-tube heat-exchanger system) and an example of a real application (two-tanks system). A comparison with two fault-detection methods has also been included. Copyright (c) 2013 John Wiley & Sons, Ltd.
Keywords:
fault detection
data driven
nonparametric identification
interval predictor
nonlinear systems
bounded error
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