Abstract
This paper addresses the problem of simultaneous actuator, process and sensor Fault Detection and Isolation (FDI) for nonlinear system having flatness properties with the presence of disturbances and which are operating in closed-loop. In particular, the nonlinear system is corrupted with additive actuator, process or sensor faults with simultaneous occurrence. In this case, the residual signals might be sensitive to all of these faults that can appear in the system. The proposed FDI method is based on both input and parameter estimators that are designed in parallel. With the flatness property of such system, the design of these two estimators requires information on the measured outputs and their successive derivatives. To estimate these last one, a new scheme of the 2nd-order dynamic sliding mode differentiator is proposed. Residuals are next defined as the difference between the estimated and expected behavior. In order to isolate the faults, dynamic neural networks technique is employed. Besides, comparative study between this new differentiator and the well-known 2nd-order Levant’s differentiator is provided to show the pros and cons of the proposed FDI method. This latter is validated by the simulation results and is carried out on a three tank system.
Highlights
Fault Detection and Isolation (FDI) problem has received a great deal of attention especially for systems for which the faults occurrence can lead to irreparable damages
This paper addresses the problem of simultaneous actuator, process and sensor Fault Detection and Isolation (FDI) for nonlinear system having flatness properties with the presence of disturbances and which are operating in closed-loop
The proposed FDI method is based on both input and parameter estimators that are designed in parallel
Summary
FDI problem has received a great deal of attention especially for systems for which the faults occurrence can lead to irreparable damages. FDI methods can be classified in two approaches: process history based methods and model-based ones. The first approach does not need any knowledge of the process mathematical model. This can be a main interest over the second category of FDI approach. Its main disadvantage is that it requires knowledge of a large amount of data. This method cannot deal with the effects caused by measurement noises. It cannot proceed in the real time case
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