Spatial basis functions based fault localisation for linear parabolic distributed parameter systems. (English) Zbl 1542.93185
Summary: Fault localisation for distributed parameter systems is as important as fault detection but is seldom discussed in the literature. The main reason is that an infinite number of sensors in the space are needed to construct a distributed residual signal, which is nearly impossible in practice. In this study, a fault detection and localisation filter which only uses a finite number of sensors is initiated based on an approximated ordinary differential equation model. Considering the limitations on computation resources for higher-order models in practice, a novel set of spatial basis functions is applied to the reduced-order fault detection and localisation filter design. Under certain conditions, the novel spatial basis functions obtain smaller state truncation error while the order is lower compared to the mostly used eigenfunctions of the spatial operator.
© 2021 The Authors. IET Control Theory & Applications published by John Wiley & Sons, Ltd. on behalf of The Institution of Engineering and Technology
© 2021 The Authors. IET Control Theory & Applications published by John Wiley & Sons, Ltd. on behalf of The Institution of Engineering and Technology
MSC:
| 93C20 | Control/observation systems governed by partial differential equations |
| 35K99 | Parabolic equations and parabolic systems |
| 93C05 | Linear systems in control theory |
| 93B11 | System structure simplification |
Keywords:
eigenvalues and eigenfunctions; fault diagnosis; reduced order systems; distributed parameter systems; differential equations; linear parabolic distributed parameter systems; fault localisation; distributed residual signal; approximated ordinary differential equation model; higher-order models; spatial basis functions; reduced-order fault detection and localisation filter design; spatial operatorReferences:
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