Fault Detection and Diagnosis in Nonlinear Systems: A by Rafael Martinez-Guerra, Juan Luis Mata-Machuca

By Rafael Martinez-Guerra, Juan Luis Mata-Machuca

The excessive reliability required in commercial tactics has created the need of detecting irregular stipulations, referred to as faults, whereas tactics are working. The time period fault generically refers to any form of method degradation, or degradation in gear functionality as a result of adjustments within the process's actual features, method inputs or environmental stipulations. This booklet is set the basics of fault detection and analysis in quite a few nonlinear platforms that are represented by way of traditional differential equations. The fault detection challenge is approached from a differential algebraic point of view, utilizing residual turbines dependent upon high-gain nonlinear auxiliary structures (‘observers’). A sought after function is performed through the kind of mathematical instruments that might be used, requiring wisdom of differential algebra and differential equations. particular theorems adapted to the desires of the problem-solving approaches are built and proved. functions to real-world difficulties, either with consistent and time-varying faults, are made during the e-book and comprise electromechanical positioning platforms, the continual Stirred Tank Reactor (CSTR), bioreactor versions and belt force structures, to call yet a few.

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Y) ¯ → fi = hi (u, ¯ y), ¯ where In practice we need to have continuous functions hi (u, u¯ and y¯ stand for finitely many derivatives of u and y, respectively. 1. 4) Linear systems are rationally diagnosable if and only if they are diagnosable. , there are singular observation data for the diagnosability of the system. 3), when the output y is small (that is, its absolute value is small), then the system becomes non diagnosable. This is to say, y˜ =0 and u˜ arbitrary are singular observation data for the diagnosability of the system [6, 7].

We have assumed that the failure modes do not occur simultaneously. 5) is algebraically observable in the Diop-Fliess’s observability sense, that is, the output system and its time derivatives satisfy an algebraic polynomial with coefficients in R(t) u, y (meromorphic functions field): For the output y1 = x1 , we have 26 2 Fault Detection in CSTR Using Nonlinear Observers x1 − y1 = 0 E/R x2 + Ts + ln . 5) is unobservable in a region determined by the algebraic varieties mentioned below [5, 6, 11]: Fs 1 1) y1 − FVs Co − FVs + ko Cs 1 − e−( V +ko )t = 0 ( FVs +ko ) 2) y1 +Cs = 0 Fs 3) y1 − (Co −Cs ) 1 − e− V t = 0 4) y1 +Cs = 0, Fs y1 − (Co −Cs ) 1 − e− V t = 0 .

2. Let us consider the nonlinear system with one fault ( f1 ) on the actuator and one fault ( f2 ) on the sensor of output y1 . ⎧ x˙1 = x1 x2 + f1 + u ⎪ ⎪ ⎨ x˙2 = x1 . 5) is diagnosable and the faults can be reconstructed from the knowledge of u, y and their time derivatives. 3. The diagnosability condition is independent of the observability of a system [6, 7]. 3. Let us consider the system ⎧ x˙1 = x1 x2 + f + u ⎪ ⎪ ⎨ x˙2 = x1 . 7) In this case f is diagnosable. However, x3 is not algebraically observable.

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