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1

Data-driven techniques for the fault diagnosis of a wind turbine benchmark

Simani S., Farsoni S., Castaldi P.

International Journal of Applied Mathematics and Computer Science

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2018

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Vol. 28, no. 2

247--268

EN

This paper deals with the fault diagnosis of wind turbines and investigates viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator, i.e., the fault estimate, involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the proposed data-driven solutions rely on fuzzy systems and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive models with exogenous input, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind turbine. The achieved performances are also compared with those of other model-based strategies from the related literature. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances.

2

Residual generator fuzzy identification for automotive diesel engine fault diagnosis

Simani S.

International Journal of Applied Mathematics and Computer Science

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2013

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Vol. 23, no. 2

419--438

EN

Safety in dynamic processes is a concern of rising importance, especially if people would be endangered by serious system failure. Moreover, as the control devices which are now exploited to improve the overall performance of processes include both sophisticated control strategies and complex hardware (input-output sensors, actuators, components and processing units), there is an increased probability of faults. As a direct consequence of this, automatic supervision systems should be taken into account to diagnose malfunctions as early as possible. One of the most promising methods for solving this problem relies on the analytical redundancy approach, in which residual signals are generated. If a fault occurs, these residual signals are used to diagnose the malfunction. This paper is focused on fuzzy identification oriented to the design of a bank of fuzzy estimators for fault detection and isolation. The problem is treated in its different aspects covering the model structure, the parameter identification method, the residual generation technique, and the fault diagnosis strategy. The case study of a real diesel engine is considered in order to demonstrate the effectiveness the proposed methodology.

3

Friction compensation in nonlinear dynamical systems using fault-tolerant control methods

Bonfè M., Castaldi P., Preda N., Simani S.

Diagnostyka

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2013

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Vol. 14, No. 4

13--19

EN

This paper describes the application of differential geometry and nonlinear systems analysis to the estimation of friction effects in a class of mechanical systems. The proposed methodology relies on adaptive filters, designed with a nonlinear geometric approach to obtain the disturbance de-coupling property, for the estimation of the friction force. Thanks to accurate estimation, friction effects are compensated by injecting the on-line estimate of friction force to the control action calculated by a standard linear state feedback. The inverted pendulum on a cart is considered as an application example and the proposed approach is compared with a commonly used friction compensation strategy, based on an explicit model of the friction force.

4

Active fault tolerant control of nonlinear systems: the cart-pole example

Bonfe M., Castaldi P., Mimmo N., Simani S.

International Journal of Applied Mathematics and Computer Science

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2011

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Vol. 21, no 3

441-455

EN

This paper describes the design of fault diagnosis and active fault tolerant control schemes that can be developed for nonlinear systems. The methodology is based on a fault detection and diagnosis procedure relying on adaptive filters designed via the nonlinear geometric approach, which allows obtaining the disturbance de-coupling property. The controller reconfiguration exploits directly the on-line estimate of the fault signal. The classical model of an inverted pendulum on a cart is considered as an application example, in order to highlight the complete design procedure, including the mathematical aspects of the nonlinear disturbance de-coupling method based on the nonlinear differential geometry, as well as the feasibility and efficiency of the proposed approach. Extensive simulations of the benchmark process and Monte Carlo analysis are practical tools for assessing experimentally the robustness and stability properties of the developed fault tolerant control scheme, in the presence of modelling and measurement errors. The fault tolerant control method is also compared with a different approach relying on sliding mode control, in order to evaluate benefits and drawbacks of both techniques. This comparison highlights that the proposed design methodology can constitute a reliable and robust approach for application to real nonlinear processes.