170,205 research outputs found
PWA Dynamic Identification for Nonlinear Model Fault Detection
No full textThis paper addresses the identification of non–linear dynamic systems. A wide class of these systems can be described using nonlinear time-invariant regression models, that can be approximated by means of piecewise affine prototypes with an arbitrary degree of accuracy. This work concerns the identification of piecewise affine model structure through inputoutput data acquired from a dynamic process. In order to show the effectiveness of the developed technique, when exploited also for FDI purpose, the results obtained in the identification of both a simple simulated system and a real dynamic process are reported
Fault diagnosis of a wind turbine simulated model via neural networks
No full textThe fault diagnosis of wind turbine systems has been proven to be a challenging task and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of wind turbines, and it proposes viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves a data-driven approach, as it represents an effective tool for coping with a poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the data-driven proposed solution relies on neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen network architecture belongs to the nonlinear autoregressive with exogenous input topology, as it can represent a dynamic evolution of the system along time. The developed fault diagnosis scheme is 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 compared with those of other control strategies, coming from the related literature. Moreover, a Monte Carlo analysis validates the robustness of the proposed solutions against the typical parameter uncertainties and disturbances
Active Fault Tolerant Control of a Wind Farm System
No full textIn order to enhance the 'sustainability’ of offshore wind farms, thus skipping unplanned maintenance operations and costs, that can be important for offshore systems, the earlier management of faults represents the key point. Therefore, this work studies the development of an adaptive sustainable control scheme with application to a wind farm benchmark consisting of nine wind turbine systems. They are described via their nonlinear models, as well as the wind and wake effects among the wind turbines of the wind park. The fault tolerant control strategy uses the recursive estimation of the faults provided by nonlinear estimators designed via a nonlinear differential algebraic tool. This aspect of the study, together with the more straightforward solution based on a data-driven scheme, is the key issue when on-line applications are proposed for a viable implementation of the proposed solutions
Parametric Identification for Robust Fault Detection
No full textThe work presents some simulation results concerning the application of robust model–based fault diagnosis to an industrial process by using identification and disturbance de–coupling techniques. The first step of the considered approach identifies several equation error models by means of the input–output data acquired from the monitored system. Each model describes the different working conditions of the plant. In particular, the equation error term of the identified models takes into account disturbances (non–measurable inputs), non–linear and time–invariant terms, measurement errors, etc. The next step of this method exploits state–space realization of the input–output equation error models allowing to define several equivalent disturbance distribution matrices related to the error terms. Moreover, in order to achieve good robustness properties for a process normally working at different operating points, a single optimal equivalent disturbance distribution matrix is selected. Finally, eigenstructure assignment method for robust residual generation and disturbance de–coupling can be successfully exploited for the fault diagnosis of the dynamic process. The fault diagnosis procedure is applied to a benchmark simulation of a gas turbine process
Fault diagnosis and fault‐tolerant control in aerospace systems
No full textModern technological and safety‐critical systems rely on sophisticated control solutions to meet increased performance demands in faulty conditions and in terms of reliability and safety requirements. A conventional feedback control design for a complex system may give unsatisfactory performance or even instability, in the event of malfunctions in actuators, sensors, or other system components. To overcome this limitation, new approaches to control system design have been developed in order to tolerate component malfunctions while maintaining desirable stability and performance properties. This feature is particularly important for safety‐critical systems, such as aircraft and spacecraft. In such plants, the consequences of a minor (abrupt or incipient) fault in a system component can be catastrophic. Therefore, the demand on reliability, safety, availability, and fault tolerance is generally high. It is necessary to design control strategies that are capable of tolerating potential faults in order to improve reliability, safety, and availability while providing desirable performances. These types of control systems are known as fault‐tolerant control systems. In more detail, they consist of control systems possessing the ability to accommodate component faults automatically. They are also capable of maintaining overall system stability and acceptable performance in the event of such faults. In other words, a closed‐loop control system that can tolerate component malfunctions, while maintaining desirable performance and stability properties is considered to be a fault‐tolerant control system
Adaptive Signal Processing Strategy for a Wind Farm System Fault Accommodation
No full textIn order to improve the availability of offshore wind farms, thus avoiding unplanned operation and maintenance costs, which can be high for offshore installations, the accommodation of faults in their earlier occurrence is fundamental. This paper addresses the design of an active fault tolerant control scheme that is applied to a wind park benchmark of nine wind turbines, based on their nonlinear models, as well as the wind and interactions between the wind turbines in the wind farm. Note that, due to the structure of the system and its control strategy, it can be considered as a fault tolerant cooperative control problem of an autonomous plant. The controller accommodation scheme provides the on-line estimate of the fault signals generated by nonlinear filters exploiting the nonlinear geometric approach to obtain estimates decoupled from both model uncertainty and the interactions among the turbines. This paper proposes also a data-driven approach to provide these disturbance terms in analytical forms, which are subsequently used for designing the nonlinear filters for fault estimation. This feature of the work, followed by the simpler solution relying on a data-driven approach, can represent the key point when on-line implementations are considered for a viable application of the proposed scheme
Data-Driven Design of Fuzzy Logic Fault Tolerant Control for a Wind Turbine Benchmark
No full textThis paper proposes a fuzzy modelling and identification approach oriented to the design of a fault tolerant fuzzy controller for regulating both the pitch angle and the reference torque of a wind turbine benchmark. This strategy has been suggested for enhancing the regulator design that could represent an alternative to the standard switching controller, already implemented in the wind turbine test system. The controller project requires the knowledge of a dynamic model of the wind turbine, which is achieved by means of a fuzzy modelling and identification scheme.
On the other hand, the proposed fuzzy controller structure is straightforward and easy to implement with respect to different strategies proposed in literature. Moreover, by exploiting the fuzzy identification procedure, the proposed strategy is also able to provide a fault tolerant controller. In this way, the fault tolerance properties are thus achieved by using a so called passive scheme. The results obtained with the designed fuzzy controller are compared to those of a switching controller already implemented for the wind turbine benchmark
Adaptive Fault–Tolerant Control Design Approach for a Wind Turbine Benchmark
No full textThis paper addresses the development of an active fault tolerant control scheme that is applied to a wind turbine simulated benchmark. The proposed methodology is based on a comprehensive scheme relying on adaptive controllers designed by means of the on–line identification of the system model under diagnosis. In this way, the controller reconfiguration
mechanism exploits an adaptive regulator implementation, depending on the on–line estimate of system model. One of the advantages of this strategy is that, for example, the original
structure of logic–based switching digital controller scheme already implemented for the wind turbine benchmark can be almost preserved. The active fault tolerant control scheme is therefore
applied to a wind turbine simulated benchmark, in the presence of disturbance and measurement errors, along nominal operating conditions, including also different realistic fault situations. The
achieved results in both fault–free and faulty conditions serve to show the enhancement of the control performances, and the fault accommodation features
Identification and fault diagnosis of nonlinear dynamic processes using hybrid models
No full textThis work addresses a novel approach for fault diagnosis of industrial processes using hybrid models. A nonlinear dynamic process can, in fact, be described as a composition of different affine submodels selected according to the process operating conditions. This paper concerns the identification of hybrid model parameters through input-output data affected by additive noise. The fault detection scheme adopted to generate residuals uses the estimated hybrid model. In order to show the effectiveness of the developed technique, the results obtained in the fault diagnosis of a real industrial plant are reported
A study of fault diagnosis and recovery techniques for manufacturing systems
No full textThis chapter describes a framework for the development of a diagnosis methodology for industrial manufacturing systems. The aim of the project is to support technicians that supervise the manufacturing plant to identify the causes of faults and failures on the machine and, in particular, to indicate a procedure for the recovery of its working condition. The chapter presents a study as the first step of a design project whose objective is to realize a supervisory system with advanced features devoted to Faults Detection and Isolation (FDI) for the manufacturing industry, with an emphasis on its integration with Human-Machine Interfaces. The requirements described in the chapter are defined giving particular care to the peculiarities of the application domain to allow the design of a powerful, but easy to use for industrial technicians, diagnostic system. An example of a manufacturing machine quite common in the packaging industry is schematized in the chapter, which is analyzed to define the fault trees for the most critical failure modes. © 2007 Copyright © 2007 Elsevier Ltd All rights reserved
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