The paper focuses on the problem of robust fault detection using neurofuzzy model based strategies. The main objective of the work is to show how to employ bounding error approach to determine the uncertainty of the neurofuzzy model and next utilize this knowledge for robust fault detection. The paper presents also how to tackle the problem of choosing the right structure of the neurofuzzy mode...

Operations, maintenance and reliability of wind turbines have received much attention over the years due to the rapid expansion of wind farms. This paper explores early fault diagnosis technique for a 5MW wind turbine system subjected to multiple faults, where genetic optimization algorithm is employed to make the residual sensitive to the faults, but robust against disturbances. The proposed t...

In the present work, we use an adaptive robust approach for fault detection and accommodation in electro-hydraulic systems. It is well known fact that any realistic model of a hydraulic system suffers from significant extent of uncertain nonlinearities and parametric uncertainties. An adaptive robust scheme is robust to such uncertainties and tracks the change in parameters reliably. Consequent...

In this paper, in order to increase the efficiency, to reduce the cost and to prevent the failures of wind turbines, which lead to an extensive break down, a robust fault diagnosis system is proposed for V47/660kW wind turbine operated in Manjil wind farm, Gilan province, Iran. According to the acquired data from Iran wind turbine industry, common faults of the wind turbine such as sensor fault...

AbstractIn this paper, a robust sensor fault detection and isolation (FDI) method based on the unknown input observer (UIO) approach is presented. The basic principle of unknown input observers is to decouple disturbances from the state estimation error. A single full-order observer is designed to detect sensor faults in the presence of unknown inputs (disturbances). By doing so, we generate a ...

This paper deals with a fault detection for a magnetic suspension system by using Generalized Internal Model Control (GIMC) structure. To design robust fault detection filters, two fault detection design problems are formulated as multiple objective optimization problems by minimizing the effects of disturbances and maximizing the fault sensitivity involving an LTI system with disturbance and f...

We propose a robust actuator fault tolerant control strategy for systems with linear parameter varying (LPV) uncertainty model description. The scheme employs a set-based robust fault detection and identification (FDI) approach and a bank of virtual actuators (VA). An interesting feature is that the virtual actuators are used both for FDI and controller reconfiguration (CR) tasks. The robust FD...

This paper presents a new approach to the design of a robust observer-based fault detection scheme for diagnosing incipient faults, called H∞ robust fault detection observer (RFDO). It takes into account the robustness of the fault detection observer against disturbances and sensitivity to faults simultaneously. The approach has originated from the robust H∞ estimator which minimizes the effect...

This paper investigates the problem of robust fault detection for discretetime switched systems with state delays. The objective is to design a robust fault detection filter that guarantees both sensitivity to faults and robustness to disturbances. Based on switched Lyapunov functional method, sufficient conditions for the existence of the above filters are given in terms of linear matrix inequ...

An Iterative Learning Observer (ILO) updated successively and iteratively by immediate past system output error and ILO input is proposed for a class of time-delay nonlinear systems for the purpose of robust fault diagnosis. The proposed observer can estimate the system state as well as disturbances and actuator faults so that ILO can still track the post-fault system. In addition, the observer...