Hardware-In-The-Loop Assessment of Fuzzy and Neural Network Fault Diagnosis Schemes for a Wind Turbine Model

The fault diagnosis of wind turbines includes extremely challenging aspects that motivate the research issues considered in this paper. In particular, this work studies fault diagnosis solutions that are considered in a viable way and used as advanced techniques for condition monitoring of dynamic processes. To this end, the work proposes the design of fault diagnosis techniques that exploits the estimation of the fault by means of data-driven approaches. These fuzzy and neural network structures are integrated with auto-regressive with exogenous input regressors, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. The capabilities of fault diagnosis schemes are validated by using a real-time simulator of a wind turbine system. Moreover, at this stage the benchmark is also useful to analyse the robustness and the reliability characteristics of the developed tools in the presence of model-reality mismatch and modelling error effects featured by the wind turbine simulator. This realistic simulator relies on a hardware-in-the-loop tool that is finally implemented for verifying and validating the performance of the developed fault diagnosis strategies in an actual environment

Wind turbine simulator fault diagnosis via fuzzy modelling and identification techniques

For improving the safety and the reliability of wind turbine installations, the earliest and fastest fault detection and isolation are highly required, since it could be used also for accommodation purpose. Modern wind turbines consist of several important subsystems, which can be affected by malfunctions regarding actuators, sensors, and components. From the turbine control point-of-view they are extremely important since provide the actuation signals, the main functions, as well as the measurements. In this paper, a fault diagnosis scheme based on the identification of fuzzy models is described, in order to detect and isolate these faults in the most efficient way, in order also to improve the energy cost, the production rate, and reduce the operation and maintenance operations. Fuzzy systems are proposed here since the model under investigation is nonlinear, whilst the wind speed measurement is uncertain since it depends on the rotor plane wind turbulence effects. These fuzzy models are described as Takagi-Sugeno prototypes, whose parameters are estimated from the wind turbine measurements. The fault diagnosis methodology is thus developed using these fuzzy models, which are exploited as residual generators. The wind turbine simulator is finally employed for the validation of the obtained performances.For improving the safety and the reliability of wind turbine installations, the earliest and fastest fault detection and isolation is highly required, since it could be used also for accommodation purpose. Modern wind turbines consist of several important subsystems, which can be affected by malfunctions regarding actuators, sensors, and components. From the turbine control point–of–view they are extremely important since provide the actuation signals, the main functions, as well as the measurements. In this paper, a fault diagnosis scheme based on the identification of fuzzy models is described, in order to detect and isolated these faults in the most efficient way, in order also to improve the energy cost, the production rate, and reduce the operation and maintenance operations. Fuzzy systems are proposed here since the model under investigation is nonlinear, whilst the wind speed measurement is uncertain since it depends on the rotor plane wind turbulence effects. These fuzzy models are described as Takagi–Sugeno prototypes, whose parameters are estimated from the wind turbine measurements. The fault diagnosis methodology is thus developed using these fuzzy models, which are exploited as residual generators. The wind turbine simulator is finally employed for the validation of the obtained performances

Hardware-In-The-Loop Assessment of a Fault Tolerant Fuzzy Control Scheme for an Offshore Wind Farm Simulator

To enhance both the safety and the efficiency of offshore wind park systems, faults must be accommodated in their earlier occurrence, in order to avoid costly unplanned maintenance. Therefore, this paper aims at implementing a fault tolerant control strategy by means of a data-driven approach relying on fuzzy logic. In particular, fuzzy modelling is considered here as it enables to approximate unknown nonlinear relations, while managing uncertain measurements and disturbance. On the other hand, the model of the fuzzy controller is directly estimated from the input-output signals acquired from the wind farm system, with fault tolerant capabilities. In general, the use of purely nonlinear relations and analytic methods would require more complex design tools. The design is therefore enhanced by the use of fuzzy model prototypes obtained via a data-driven approach, thus representing the key point if real- time solutions have to implement the proposed fault tolerant control strategy. Finally, a high- fidelity simulator relying on a hardware-in-the-loop tool is exploited to verify and validate the reliability and robustness characteristics of the developed methodology also for on-line and more realistic implementations

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

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.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

Data-driven fault diagnosis of awind farm benchmark model

The fault diagnosis of wind farms 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 a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty, and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system over 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 farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and reliability of the proposed solutions against typical parameter uncertainties and disturbances.The fault diagnosis of wind farms 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 a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty, and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system over 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 farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and reliability of the proposed solutions against typical parameter uncertainties and disturbances

Chemical process disturbance compensation as a fault tolerant control problem

In general, the control of chemical processes that involve unknown disturbances presents interesting challenges. Research issues have been focused on detailed modelling of the involved phenomena in order to use e.g. robust on-line disturbance compensation procedures or attempting to cancel out the disturbance effect in the feedback control of the chemical system. However, the chemical modelling problem can remain a very difficult challenge due to the unknown or partially-known dynamics occurring in the process under investigation. Therefore, this article proposes a new approach to the disturbance compensation, which is recasted into the theory of robust fault estimation. The disturbances acting in the system can be thus viewed as faults with time-varying characteristics to be estimated and compensated within an output feedback fault-tolerant control scheme. In this way, the limitations arising from the use of model-based approaches are obviated. The unknown input estimation problem is hence embedded inside a control system with required stability and performance robustness. This can be a significant advantage over model-based unknown input compensation methods, in which the detailed modelling of the disturbance term can be essential, and for which robustness with respect to its characteristics is difficult to achieve using purely nonlinear modelling strategies

Actuator Fault Reconstruction via Dynamic Neural Networks for an Autonomous Underwater Vehicle Model

This paper proposes the development of a scheme for the fault diagnosis of the actuators of a simulated model accurately representing the behaviour of an autonomous underwater vehicle. The Fossen model usually adopted to describe the dynamics of the underwater vehicle has been generalised in this paper to take into account time-varying sea currents. The proposed fault detection and isolation strategy uses a data-driven approach relying on multi-layer perceptron neural networks that include auto-regressive exogenous prototypes that provide the fault reconstruction. These tools are thus exploited to design a bank of dynamic neural networks for residual generation that are trained on the basis of the input and outputmeasurements acquired from the simulator. In this work, the residuals are designed to represent the reconstruction of the fault signals themselves. Moreover, the neural network bank is also able to perform the isolation task, in case of simultaneous and concurrent faults affecting the actuators. The paper firstly describes the steps performed for deriving the proposed fault diagnosis solution. Secondly, the effectiveness of the scheme is demonstrated by means of high-fidelity simulations of a realistic autonomous underwater vehicle, in the presence of faults and marine current

Development and deployment of an underway radioactive cesium monitor off the Japanese coast near Fukushima Dai'ichi

A custom radiation monitoring system was developed by Oregon State University at the request of the Woods Hole Oceanographic Institute to measure radioactive cesium contaminants in the ocean waters near Fukushima Dai’ichi Nuclear Power Plant. The system was to be used on board the R/V Ka’imikai-O-Kanaloa during a 15 d research cruise to provide real-time approximations of radionuclide concentration and alert researchers to the possible occurrence of highly elevated radionuclide concentrations. A NaI(Tl) scintillation detector was coupled to a custom-built compact digital spectroscopy system and suspended within a sealed tank of continuously flowing seawater. A series of counts were acquired within an energy region corresponding to the main photopeak of ¹³⁷Cs. The system was calibrated using known quantities of radioactive ¹³⁴Cs and ¹³⁷Cs in a ratio equating to that present at the reactors’ ocean outlet. The response between net count rate and concentration of ¹³⁷Cs was then used to generate temporal and geographic plots of ¹³⁷Cs concentration throughout the research cruise in Japanese coastal waters. The concentration of ¹³⁷Cs was low but detectable, reaching a peak of 3.8 ± 0.2 Bq/L.This is the author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Elsevier and can be found at: http://www.journals.elsevier.com/journal-of-environmental-radioactivity/.Keywords: Monitor, Fukushima, Seawater, Flow-through, Dai-ichi, Radiocesiu

A Mechatronic Cardiovascular Simulation System for Jugular Venous Echo-Doppler Training

Echo-doppler examination of the jugular vessel is a powerful tool for the early diagnosis of cardiovascular disorders that can be further related to central nervous system diseases. Unfortunately, the ultrasound technique is strongly operator-dependent, so the quality of the scan, the accuracy of the measurement, and therefore the rapidity and robustness of the diagnosis reflect the degree of training. The paper presents the development of a mechatronic simulation system for improving the skill of novice physicians in echo-doppler procedures. The patient is simulated by a silicone manikin whose materials are designed to have a realistic ultrasound response. Two tubes allow blood-mimicking fluid to flow inside the manikin, simulating the hemodynamics of the internal jugular vein. The mechatronic system is designed for controlling the flow waveform, to reproduce several clinical cases of interest for diagnosis. The experiments investigate the accuracy of the echo-doppler measurements performed on the proposed system by novice operators using a real ultrasound scanner

Fault-tolerant control of an offshore wind farm via fuzzy modelling and identification

In order to improve the safety, the reliability, and the efficiency of offshore wind park installations, thus avoiding expensive unplanned maintenance, the accommodation of faults in their earlier occurrence is fundamental. Therefore, the main contribution of this paper consists of the development of a fault tolerant control scheme by means of a viable approach. In particular, a data-driven strategy based on fuzzy logic is exploited for deriving the model of the required controller. Fuzzy theory is exploited here since it is able to approximate easily unknown nonlinear models and manage noisy measurements. Moreover, the controller fuzzy prototype is directly identified from the wind farm measurements, and it provides the straightforward achievement of the fault tolerant control scheme. In general, an analytic approach, where the system nonlinearity is explicitly taken into account, could require more complex design methodologies. This aspect of the work, followed by the simpler solution relying on fuzzy rules, represents the key point when on-line implementations are considered of the proposed control scheme. To highlight the potential of the proposed fault tolerant control algorithm in real applications, a hardware-in-the-loop test facility representing a realistic offshore wind farm installation is considered to analyse the digital implementation of the designed controller. The results have shown that the developed scheme maintains desired performances, thus validating its reliability also in real-time implementations.In order to improve the safety, the reliability, and the efficiency of offshore wind park installations, thus avoiding expensive unplanned maintenance, the accommodation of faults in their earlier occurrence is fundamental. Therefore, the main contribution of this paper consists of the development of a fault tolerant control scheme by means of a viable approach. In particular, a data-driven strategy based on fuzzy logic is exploited for deriving the model of the required controller. Fuzzy theory is exploited here since it is able to approximate easily unknown nonlinear models and manage noisy measurements. Moreover, the controller fuzzy prototype is directly identified from the wind farm measurements, and it provides the straightforward achievement of the fault tolerant control scheme. In general, an analytic approach, where the system nonlinearity is explicitly taken into account, could require more complex design methodologies. This aspect of the work, followed by the simpler solution relying on fuzzy rules, represents the key point when on-line implementations are considered of the proposed control scheme. To highlight the potential of the proposed fault tolerant control algorithm in real applications, a hardware–in– the–loop test facility representing a realistic offshore wind farm installation is considered to analyse the digital implementation of the designed controller. The results have shown that the developed scheme maintains desired performances, thus validating its reliability also in real-time implementations