This article considers the design of an input signal for improving the diagnosability of faults from process measurements. Previous work has focused on open-loop input design. In particular, deterministic methods are ...
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ISBN:
(纸本)9781479901890
This article considers the design of an input signal for improving the diagnosability of faults from process measurements. Previous work has focused on open-loop input design. In particular, deterministic methods are available for computing an input that guarantees fault diagnosis within a specified time horizon, whenever such an input exists. Here, two closed-loop approaches are considered that use feedback in order to reduce the length and/or cost of the required input, while maintaining this guarantee. The first method uses an existing open-loop input design method within a receding horizon framework. The second method approximates the first by an explicit feedback law in order to reduce online computations.
A robust control method is presented for linear systems subject to input and state constraints, bounded disturbances and measurement noise, and discrete faults in sensors, actuators, and system dynamics. The approach ...
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ISBN:
(纸本)9781479928569
A robust control method is presented for linear systems subject to input and state constraints, bounded disturbances and measurement noise, and discrete faults in sensors, actuators, and system dynamics. The approach uses set-based fault detection and isolation techniques to coordinate switching between controllers designed for each fault scenario. In contrast to previous approaches, the fault isolation method uses an active input designed to guarantee isolability subject to input and state constraints.
Effective fault diagnosis depends on the detectability of the faults in the measurements, which can be improved by a suitable input signal. This article presents a deterministic method for computing the set of inputs ...
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ISBN:
(纸本)9781479901777
Effective fault diagnosis depends on the detectability of the faults in the measurements, which can be improved by a suitable input signal. This article presents a deterministic method for computing the set of inputs that guarantee fault diagnosis, referred to as separating inputs. The process of interest is described, under nominal and various faulty conditions, by linear discrete-time models subject to bounded process and measurement noise. It is shown that the set of separating inputs can be efficiently computed in terms of the complement of one or several zonotopes, depending on the number of fault models. In practice, it is essential to choose elements from this set that are minimally harmful with respect to other control objectives. It is shown that this can be done efficiently through the solution of a mixed-integer quadratic program. The method is demonstrated for a numerical example.
The problem of designing an input to improve the detectability of faults has been addressed previously using both stochastic and deterministic formulations. This article presents a hybrid approach that provides a wors...
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ISBN:
(纸本)9781467357159
The problem of designing an input to improve the detectability of faults has been addressed previously using both stochastic and deterministic formulations. This article presents a hybrid approach that provides a worst-case guarantee of diagnosis within a time interval [0,N], while maximizing the probability of diagnosis at some earlier time M < N. Compared to purely deterministic methods, this strategy reduces the average time required for diagnosis. Moreover, a high probability of diagnosis at M enables N to be chosen large, thus reducing the conservatism of the required input.
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