Condition monitoring of an advanced gas-cooled nuclear reactor core

A critical component of an advanced gas-cooled reactor station is the graphite core. As a station ages, the graphite bricks that comprise the core can distort and may eventually crack. Since the core cannot be replaced, the core integrity ultimately determines the station life. Monitoring these distortions is usually restricted to the routine outages, which occur every few years, as this is the only time that the reactor core can be accessed by external sensing equipment. This paper presents a monitoring module based on model-based techniques using measurements obtained during the refuelling process. A fault detection and isolation filter based on unknown input observer techniques is developed. The role of this filter is to estimate the friction force produced by the interaction between the wall of the fuel channel and the fuel assembly supporting brushes. This allows an estimate to be made of the shape of the graphite bricks that comprise the core and, therefore, to monitor any distortion on them

Leak localization in water distribution networks using a mixed model-based/data-driven approach

“The final publication is available at Springer via http://dx.doi.org/10.1016/j.conengprac.2016.07.006”This paper proposes a new method for leak localization in water distribution networks (WDNs). In a first stage, residuals are obtained by comparing pressure measurements with the estimations provided by a WDN model. In a second stage, a classifier is applied to the residuals with the aim of determining the leak location. The classifier is trained with data generated by simulation of the WDN under different leak scenarios and uncertainty conditions. The proposed method is tested both by using synthetic and experimental data with real WDNs of different sizes. The comparison with the current existing approaches shows a performance improvement.Peer ReviewedPostprint (author's final draft

A self-validating control system based approach to plant fault detection and diagnosis

An approach is proposed in which fault detection and diagnosis (FDD) tasks are distributed to separate FDD modules associated with each control system located throughout a plant. Intended specifically for those control systems that inherently eliminate steady state error, it is modular, steady state based, requires very little process specific information and therefore should be attractive to control systems implementers who seek economies of scale. The approach is applicable to virtually all types of process plant, whether they are open loop stable or not, have a type or class number of zero or not and so on. Based on qualitative reasoning, the approach is founded on the application of control systems theory to single and cascade control systems with integral action. This results in the derivation of cause-effect knowledge and fault isolation procedures that take into account factors like interactions between control systems, and the availability of non-control-loop-based sensors

Fault Detection and Isolation of Nonlinear Systems with Generalized Hamiltonian Representation

The problem of fault diagnosis in a class of nonlinear system is considered. Systems that can be written in the so‐called Generalized Hamiltonian Representation (which is equivalent to an Euler‐Lagrange representation) are studied, and a model‐based observer approach for this class of systems is developed. The main advantage of the proposed approach is the facility to design the required observers, which take advantage of the system structure given by the Hamitonian representation. In order to show the proposed schema, a model of a permanent magnet synchronous machine is revised and the fault diagnosis schema presented. Simulation results confirm the effectivity of the proposed schema

An Integrated Approach to Performance Monitoring and Fault Diagnosis of Nuclear Power Systems

In this dissertation an integrated framework of process performance monitoring and fault diagnosis was developed for nuclear power systems using robust data driven model based methods, which comprises thermal hydraulic simulation, data driven modeling, identification of model uncertainty, and robust residual generator design for fault detection and isolation. In the applications to nuclear power systems, on the one hand, historical data are often not able to characterize the relationships among process variables because operating setpoints may change and thermal fluid components such as steam generators and heat exchangers may experience degradation. On the other hand, first-principle models always have uncertainty and are often too complicated in terms of model structure to design residual generators for fault diagnosis. Therefore, a realistic fault diagnosis method needs to combine the strength of first principle models in modeling a wide range of anticipated operation conditions and the strength of data driven modeling in feature extraction. In the developed robust data driven model-based approach, the changes in operation conditions are simulated using the first principle models and the model uncertainty is extracted from plant operation data such that the fault effects on process variables can be decoupled from model uncertainty and normal operation changes. It was found that the developed robust fault diagnosis method was able to eliminate false alarms due to model uncertainty and deal with changes in operating conditions throughout the lifetime of nuclear power systems. Multiple methods of robust data driven model based fault diagnosis were developed in this dissertation. A complete procedure based on causal graph theory and data reconciliation method was developed to investigate the causal relationships and the quantitative sensitivities among variables so that sensor placement could be optimized for fault diagnosis in the design phase. Reconstruction based Principal Component Analysis (PCA) approach was applied to deal with both simple faults and complex faults for steady state diagnosis in the context of operation scheduling and maintenance management. A robust PCA model-based method was developed to distinguish the differences between fault effects and model uncertainties. In order to improve the sensitivity of fault detection, a hybrid PCA model based approach was developed to incorporate system knowledge into data driven modeling. Subspace identification was proposed to extract state space models from thermal hydraulic simulations and a robust dynamic residual generator design algorithm was developed for fault diagnosis for the purpose of fault tolerant control and extension to reactor startup and load following operation conditions. The developed robust dynamic residual generator design algorithm is unique in that explicit identification of model uncertainty is not necessary. Finally, it was demonstrated that the developed new methods for the IRIS Helical Coil Steam Generator (HCSG) system. A simulation model was first developed for this system. It was revealed through steady state simulation that the primary coolant temperature profile could be used to indicate the water inventory inside the HCSG tubes. The performance monitoring and fault diagnosis module was then developed to monitor sensor faults, flow distribution abnormality, and heat performance degradation for both steady state and dynamic operation conditions. This dissertation bridges the gap between the theoretical research on computational intelligence and the engineering design in performance monitoring and fault diagnosis for nuclear power systems. The new algorithms have the potential of being integrated into the Generation III and Generation IV nuclear reactor I&C design after they are tested on current nuclear power plants or Generation IV prototype reactors

Fault Diagnosis Via Univariate Frequency Analysis Monitoring: A Novel Technique Applied to a Simulated Integrated Drive Generator

The purpose of this research was to develop a fault detection and diagnostic method that would be able to detect and isolate seeded faults in data that was generated from a simulated integrated drive generator. The approach to the solution for this problem is summarized below. A novel approach for the detection and diagnoses of an anomaly due the occurrence of a fault within a system has been developed. This innovative technique uses specific characteristics of the frequency spectrum of a univariate signal to monitor system health for abnormal behavior due to previously characterized component failure. A fault detection and diagnostic scheme was developed that used dual heteroassociative kernel regression models. The first of these empirical models estimates selected features from the analytical redundant spectrum characteristic profile of the exciter current using power demand, a stressor, placed on the system as input query. The predicted spectrum features were compared to the actual characteristic features, which resulted in the generation of a residual signal. This signal was then analyzed in order to determine if they were the result of normal system disturbances or a predefined fault. If a fault was detected, the residual signal was passed to the second model, which isolated, and given enough information, identified the specific component of components causing the anomaly. Two case studies are presented to illustrate the capability to detect, isolate, and identify a system anomaly. As demonstrated, the monitoring of the frequency spectrum of a single variable can provide adequate indication of equipment health. With the availability of the appropriate data, as in the first case, it is possible for the development of three-layer detection and diagnostic systems that provides fault detection, isolation, and identification. A three-layer detection and diagnostic system is essential in the development of more advance health monitoring and prognostic systems. Despite some shortcomings in the simulated data made available for this work, this method is believed to be applicable to data that more realistically captures real-world relationships, including sensor noise and faults that grow with time

F-8C adaptive flight control extensions

An adaptive concept which combines gain-scheduled control laws with explicit maximum likelihood estimation (MLE) identification to provide the scheduling values is described. The MLE algorithm was improved by incorporating attitude data, estimating gust statistics for setting filter gains, and improving parameter tracking during changing flight conditions. A lateral MLE algorithm was designed to improve true air speed and angle of attack estimates during lateral maneuvers. Relationships between the pitch axis sensors inherent in the MLE design were examined and used for sensor failure detection. Design details and simulation performance are presented for each of the three areas investigated