Model-based Fault Diagnosis Techniques: Design Schemes, by Steven X. Ding

By Steven X. Ding

A most important and demanding factor surrounding the layout of automated keep an eye on structures with the successively expanding complexity is ensuring a excessive process functionality over a large working diversity and assembly the necessities on method reliability and dependability. As one of many key applied sciences for the matter options, complex fault detection and identity (FDI) know-how is receiving substantial recognition.

The aim of this ebook is to introduce uncomplicated model-based FDI schemes, complex research and layout algorithms and the wanted mathematical and keep watch over idea instruments at a degree for graduate scholars and researchers in addition to for engineers.

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Also in the H4 framework, transforming the robust FDI problems into the so-called Model-Matching-Problem (MMP), a standard problem formulation in the H4 framework, provides an alternative FDI system design scheme. This work has been particularly driven by the so-called integrated design of feedback controller and (observer-based) FDI system, and the achieved results have also been applied for the purpose of fault identification, as described in Chapter 14. Stimulated by the recent research eorts on robust control of uncertain systems, study on the FDI in uncertain systems is receiving increasing attention in this decade.

2). 5) is the mathematical description of a change in the system output caused by the occurrence of a fault (from zero to a time function different from zero), independent of its size and type. A fault becomes detectable if this change is not constantly zero. In other words, it should dier from zero at least at some time instant and for some system input. cient condition for the detectability of additive and multiplicative faults.

The eigenvalues of matrix D are v1 = 35101> v2 = 1143=1> v3 = 442=3 + 331=7m v4 = 442=3  331=7m> v5 = 0=0= ˆ (v)> Q ˆ (v)> an observer gain matrix O has been selected: To construct P 48 3 Modelling of technical systems 5 9=2418 × 105 9 1=6676 × 103 9 7 O=9 9 5=6 × 10 4 7 3=2451 × 10 1=8795 × 107 6 3=0326 × 103 7=1992 × 104 : : 19=116 : : 1=02 × 1012 8 1=262 × 103 which results in the following eigenvalues of matrix D  OF : v1 = 38611> v2 = 1257=4> v3 = 486=5 + 364=9m v4 = 486=5  364=9m> v5 = 30000= Disturbance and model uncertainty Typical disturbance that aects the EHSA is the external air loads IO = The uncertainty due to the linearization can be described by 6 5 0 0 0 00 9 0 1 0 0 0 :  3¸ : 9 : > |1 |  1=0097 × 104 [Q ] > |2 |  1=144 × 103 p = 0 0 0 0 0 D = 9 : 9 v 7 0 0 2 0 0 8 0 0 0 00 This kind of uncertainty is of the polytopic type because they build a convex set depending on dierent operating points.

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