DECENTRALIZED AUTONOMOUS FAULT DETECTION IN WIRELESS STRUCTURAL HEALTH MONITORING SYSTEMS USING STRUCTURAL RESPONSE DATA

Sensor faults can affect the dependability and the accuracy of structural health monitoring (SHM) systems. Recent studies demonstrate that artificial neural networks can be used to detect sensor faults. In this paper, decentralized artificial neural networks (ANNs) are applied for autonomous sensor fault detection. On each sensor node of a wireless SHM system, an ANN is implemented to measure and to process structural response data. Structural response data is predicted by each sensor node based on correlations between adjacent sensor nodes and on redundancies inherent in the SHM system. Evaluating the deviations (or residuals) between measured and predicted data, sensor faults are autonomously detected by the wireless sensor nodes in a fully decentralized manner. A prototype SHM system implemented in this study, which is capable of decentralized autonomous sensor fault detection, is validated in laboratory experiments through simulated sensor faults. Several topologies and modes of operation of the embedded ANNs are investigated with respect to the dependability and the accuracy of the fault detection approach. In conclusion, the prototype SHM system is able to accurately detect sensor faults, demonstrating that neural networks, processing decentralized structural response data, facilitate autonomous fault detection, thus increasing the dependability and the accuracy of structural health monitoring systems

DECENTRALIZED AUTONOMOUS FAULT DETECTION IN WIRELESS STRUCTURAL HEALTH MONITORING SYSTEMS USING STRUCTURAL RESPONSE DATA

Sensor faults can affect the dependability and the accuracy of structural health monitoring (SHM) systems. Recent studies demonstrate that artificial neural networks can be used to detect sensor faults. In this paper, decentralized artificial neural networks (ANNs) are applied for autonomous sensor fault detection. On each sensor node of a wireless SHM system, an ANN is implemented to measure and to process structural response data. Structural response data is predicted by each sensor node based on correlations between adjacent sensor nodes and on redundancies inherent in the SHM system. Evaluating the deviations (or residuals) between measured and predicted data, sensor faults are autonomously detected by the wireless sensor nodes in a fully decentralized manner. A prototype SHM system implemented in this study, which is capable of decentralized autonomous sensor fault detection, is validated in laboratory experiments through simulated sensor faults. Several topologies and modes of operation of the embedded ANNs are investigated with respect to the dependability and the accuracy of the fault detection approach. In conclusion, the prototype SHM system is able to accurately detect sensor faults, demonstrating that neural networks, processing decentralized structural response data, facilitate autonomous fault detection, thus increasing the dependability and the accuracy of structural health monitoring systems

Robust SHM Systems Using Bayesian Model Updating

Structural Health Monitoring (SHM) is becoming increasingly important for monitoring infrastructures. However, one of the main challenges is that the changes due to aging are small, not only for structures, but also for SHM systems. Hence, the question is how should we distinguish such changes due to aging from measurement uncertainty. In this study, laser triangulation sensors (LTSs) are tested and the uncertainty due to temperature effects is studied. Furthermore, time-dependent experiments are performed and the SHM system is calibrated over time through Bayesian Model Updating, considering its temperature dependence

Methods and Systems for Fault Diagnosis in Nuclear Power Plants

This research mainly deals with fault diagnosis in nuclear power plants (NPP), based on a framework that integrates contributions from fault scope identification, optimal sensor placement, sensor validation, equipment condition monitoring, and diagnostic reasoning based on pattern analysis. The research has a particular focus on applications where data collected from the existing SCADA (supervisory, control, and data acquisition) system is not sufficient for the fault diagnosis system. Specifically, the following methods and systems are developed. A sensor placement model is developed to guide optimal placement of sensors in NPPs. The model includes 1) a method to extract a quantitative fault-sensor incidence matrix for a system; 2) a fault diagnosability criterion based on the degree of singularities of the incidence matrix; and 3) procedures to place additional sensors to meet the diagnosability criterion. Usefulness of the proposed method is demonstrated on a nuclear power plant process control test facility (NPCTF). Experimental results show that three pairs of undiagnosable faults can be effectively distinguished with three additional sensors selected by the proposed model. A wireless sensor network (WSN) is designed and a prototype is implemented on the NPCTF. WSN is an effective tool to collect data for fault diagnosis, especially for systems where additional measurements are needed. The WSN has distributed data processing and information fusion for fault diagnosis. Experimental results on the NPCTF show that the WSN system can be used to diagnose all six fault scenarios considered for the system. A fault diagnosis method based on semi-supervised pattern classification is developed which requires significantly fewer training data than is typically required in existing fault diagnosis models. It is a promising tool for applications in NPPs, where it is usually difficult to obtain training data under fault conditions for a conventional fault diagnosis model. The proposed method has successfully diagnosed nine types of faults physically simulated on the NPCTF. For equipment condition monitoring, a modified S-transform (MST) algorithm is developed by using shaping functions, particularly sigmoid functions, to modify the window width of the existing standard S-transform. The MST can achieve superior time-frequency resolution for applications that involves non-stationary multi-modal signals, where classical methods may fail. Effectiveness of the proposed algorithm is demonstrated using a vibration test system as well as applications to detect a collapsed pipe support in the NPCTF. The experimental results show that by observing changes in time-frequency characteristics of vibration signals, one can effectively detect faults occurred in components of an industrial system. To ensure that a fault diagnosis system does not suffer from erroneous data, a fault detection and isolation (FDI) method based on kernel principal component analysis (KPCA) is extended for sensor validations, where sensor faults are detected and isolated from the reconstruction errors of a KPCA model. The method is validated using measurement data from a physical NPP. The NPCTF is designed and constructed in this research for experimental validations of fault diagnosis methods and systems. Faults can be physically simulated on the NPCTF. In addition, the NPCTF is designed to support systems based on different instrumentation and control technologies such as WSN and distributed control systems. The NPCTF has been successfully utilized to validate the algorithms and WSN system developed in this research. In a real world application, it is seldom the case that one single fault diagnostic scheme can meet all the requirements of a fault diagnostic system in a nuclear power. In fact, the values and performance of the diagnosis system can potentially be enhanced if some of the methods developed in this thesis can be integrated into a suite of diagnostic tools. In such an integrated system, WSN nodes can be used to collect additional data deemed necessary by sensor placement models. These data can be integrated with those from existing SCADA systems for more comprehensive fault diagnosis. An online performance monitoring system monitors the conditions of the equipment and provides key information for the tasks of condition-based maintenance. When a fault is detected, the measured data are subsequently acquired and analyzed by pattern classification models to identify the nature of the fault. By analyzing the symptoms of the fault, root causes of the fault can eventually be identified

Distributed fault detection and isolation of large-scale nonlinear systems: an adaptive approximation approach

2007/2008The present thesis work introduces some recent and novel results about the problem of fault diagnosis for distributed nonlinear and large scale systems. The problem of automated fault diagnosis and accommodation is motivated by the need to develop more autonomous and intelligent systems that operate reliably in the presence of system faults. In dynamical systems, faults are characterized by critical and unpredictable changes in the system dynamics, thus requiring the design of suitable fault diagnosis schemes. A fault diagnosis scheme that drew considerable attention and provided remarkable results is the so called model based scheme, which is based upon a mathematical model of the healthy behavior of the system that is being monitored. At each time instant, the model is used to compute an estimate of what should be the current behavior of the system, assuming it is not affected by a fault. If the behavior of the system is characterized by the time evolution of its state vector x(t), and the inputs to the system are denoted as u(t), then the most general nonlinear and uncertain discrete time model can be represented by x(t + 1) = f (x(t), u(t)) + η(t) , where the nonlinear function f represents the nominal model of the healthy system, and η(t) is an uncertainty term. A proven way to compute an estimate of the state x(t) is by using a diagnostic observer, so that in healthy conditions the residual between the true and the estimated value is, in practice, close to zero. Should the residual cross at a certain point a suitable threshold ̄ǫ(t), the observed difference between the model estimate and the actual measurements will be explained by the presence of a fault. The model-based scheme outlined so far has showed many interesting properties and advantages over signal-based ones, but anyway poses practical implementation problems when one tries to apply it to actual distributed, large-scale systems. In fact an implicit assumption about the model-based scheme is that the task of measuring all the state and input vectors components, and the task of computing the estimate of x(t) can be done in real-time by some single and powerful computer. But for large enough systems, this assumptions cannot be fulfilled by available measurement, communication and computation hardware. This problem constitutes the motivation of the present work. It will be solved by developing decomposition strategies in order to break down the original centralized diagnosis problem into many distributed diagnosis subproblems, that are tackled by agents called Local Fault Diagnosers that have a limited view about the system, but that are allowed to communicate between neighboring agents. In order to take advantage of the distributed nature of the proposed schemes, the agents are allowed to cooperate on the diagnosis of parts of the system shared by more than one diagnoser, by using consensus techniques. Chapter 2 introduces the problem of model-based fault diagnosis by presenting recent results about the centralized diagnosis of uncertain nonlinear discrete time systems. The development of a distributed fault diagnosis architecture is covered in the key Chapter 3, while Chapters 4 and 5 show how this distributed architecture is implemented for discrete and continuous time nonlinear and uncertain large–scale systems. In every chapter an illustrative example is provided, as well as analytical results that characterize the performances attainable by the proposed architecture. ---------------------------------------------------Questo lavoro di tesi presenta alcuni risultati recenti ed innovativi sulla diagnostica di guasto per sistemi nonlineari distribuiti e su larga scala. Il problema della diagnostica automatica di guasto è motivata dal bisogno di sviluppare sistemi maggiormenti autonomi e robusti, che possano operare in modo affidabile anche in presenza di guasti. Nei sistemi dinamici, i guasti sono caratterizati da variazioni critiche ed imprevedibili della dinamica, e richiedono perciò la progettazione di schemi di diagnostica adeguati. Uno schema che ha riscosso notevole successo è il cosidetto schema basato su modello, che si fonda su un modello matematico del comportamento sano del sistema sotto osservazione. Ad ogni istante, il modello è usato per calcolare una stima di quello che dovrebbe essere il comportamento attuale, supponendo l’assenza di guasti. Se il comportamento del sistema è caratterizzato attraverso l’evoluzione temporale del vettore di stato x(t), ed il vettore degli ingressi è indicato con u(t), allora il modello più generale per un sistema non lineare ed incerto a tempo discreto è x(t + 1) = f (x(t), u(t)) + η(t) , dove la funzione nonlineare f rappresenta la dinamica del sistema sano, mentre η(t) è l’incertezza di modello. Un modo comprovato per calcolare una stima dello stato x(t) fa uso di un osservatore diagnostico, cosicché in condizioni normali il residuo tra il valore vero e quello stimato è, in pratica, quasi nullo. Se dovesse ad un certo punto superare un’opportuna soglia, la differenza osservata tra la stima del modello ed il valore vero misurato sarebbe spiegabile con la presenza di un guasto. Lo schema basato su modello riassunto finora ha mostrato molte proprietà interessanti e vantaggi rispetto quelli basati su segnali, ma pone in ogni caso problemi di tipo pratico quando lo si voglia applicare a sistemi reali distribuiti e su larga scala. Infatti un’ipotesi sottointesa dello schema basato su modello è che il compito di misurare tutte le componenti di x(t) e di u(t), e quello di calcolare la stima di x(t) possa essere portato a termine in tempo reale da un singolo nodo di calcolo. Nel caso di sistemi sufficientemente vasti, però, questa ipotesi non può essere rispettata da alcuna delle risorse di calcolo disponibili in pratica. Questo problema è alla base del presente lavoro di tesi. Verrà risolto sviluppando delle strategie di decomposizione in modo da suddividere il problema di diagnostica centralizzato in molteplici sotto-problemi distribuiti, dati in carico ad agenti detti Diagnostici Locali, che hanno una visione limitata del sistema, ma che possono comunicare con agenti vicini. In modo da sfruttare la natura distribuita dello schema proposto, gli agenti potranno cooperare sulla diagnostica di parti del sistema che siano comuni a più diagnostici, attraverso tecniche di consenso. Il Capitolo 2 introduce il problema della diagnostica basata su modello attraverso dei risultati recenti sulla diagnostica centralizzata di sistemi a tempo discreto con dinamica non lineare ed incerta. Lo sviluppo dell’architettura di diagnostica distribuita è trattato nel fondamentale Capitolo 3, mentre i Capitoli 4 e 5 mostrano come questa architettura distribuita è implementata a tempo discreto e a tempo continuo. In ogni capitolo è presente un esempio didattico, oltre a risultati analitici che caratterizzano le prestazioni ottenibili dall’architettura proposta.XX Ciclo197

Distributed Methods for Estimation and Fault Diagnosis: the case of Large-scale Networked Systems

2011/2012L’obiettivo di questa tesi è il monitoraggio di sistemi complessi a larga-scala. L’importanza di questo argomento è dovuto alla rinnovata enfasi data alle problematiche riguardanti la sicurezza e l’affidabilità dei sistemi, diventate requisiti fondamentali nella progettazione. Infatti, la crescente complessità dei moderni sistemi, dove le relazioni fra i diversi componenti, con il mondo esterno e con il fattore umano sono sempre più importanti, implica una crescente attenzione ai rischi e ai costi dovuti ai guasti e lo sviluppo di approcci nuovi per il controllo e il monitoraggio. Mentre nel contesto centralizzato i problemi di stima e di diagnostica di guasto sono stati ampiamente studiati, lo sviluppo di metodologie specifiche per sistemi distribuiti, larga scala o “networked”, come i Cyber-Physical Systems e i Systems-of-Systems, è cominciato negli ultimi anni. Il sistema fisico è rappresentato come l’interconnessione di sottosistemi ottenuti attraverso una decomposizione del sistema complesso dove le sovrapposizioni sono consentite. L’approccio si basa sul modello dinamico non-lineare dei sottosistemi e sull’approssimazione adattativa delle non note interconnessioni fra i sottosistemi. La novità è la proposta di un’architettura unica che tenga conto dei molteplici aspetti che costituiscono i sistemi moderni, integrando il sistema fisico, il livello sensoriale e il sistema di diagnostica e considerando le relazioni fra questi ambienti e le reti di comunicazione. In particolare, vengono proposte delle soluzioni ai problemi che emergono dall’utilizzo di reti di comunicazione e dal considerare sistemi distribuiti e networked. Il processo di misura è effettuato da un insieme di reti di sensori, disaccoppiando il livello fisico da quello diagnostico e aumentando in questo modo la scalabilità e l’affidabilità del sistema diagnostico complessivo. Un nuovo metodo di stima distribuita per reti di sensori è utilizzato per filtrare le misure minimizzando sia la media sia la varianza dell’errore di stima attraverso la soluzione di un problema di ottimizzazione di Pareto. Un metodo per la re-sincronizzazione delle misure è proposto per gestire sistemi multi-rate e misure asincrone e per compensare l’effetto dei ritardi nella rete di comunicazione fra sensori e diagnostici. Poiché uno dei problemi più importanti quando si considerano sistemi distribuiti e reti di comunicazione è per l’appunto il verificarsi di ritardi di trasmissione e perdite di pacchetti, si propone una strategia di compensazione dei ritardi , basata sull’uso di Time Stamps e buffer e sull’introduzione di una matrice di consenso tempo-variante, che permette di gestire il problema dei ritardi nella rete di comunicazione fra diagnostici. Gli schemi distribuiti per la detection e l’isolation dei guasti sono sviluppati, garantendo la convergenza degli stimatori e derivando le condizioni sufficienti per la detectability e l’isolability. La matrice tempo-variante proposta permette di migliorare queste proprietà definendo delle soglie meno conservative. Alcuni risultati sperimentali provano l’efficacia del metodo proposto. Infine, le architetture distribuite per la detection e l’isolation, sviluppate nel caso tempo-discreto, sono estese al caso tempo continuo e nello scenario in cui lo stato non è completamente misurabile, sia a tempo continuo che a tempo discreto.This thesis deals with the problem of the monitoring of modern complex systems. The motivation is the renewed emphasis given to monitoring and fault-tolerant systems. In fact, nowadays reliability is a key requirement in the design of technical systems. While fault diagnosis architectures and estimation methods have been extensively studied for centralized systems, the interest towards distributed, networked, large-scale and complex systems, such as Cyber-Physical Systems and Systems-of-Systems, has grown in the recent years. The increased complexity in modern systems implies the need for novel tools, able to consider all the different aspects and levels constituting these systems. The system being monitored is modeled as the interconnection of several subsystems and a divide et impera approach allowing overlapping decomposition is used. The local diagnostic decision is made on the basis of the knowledge of the local subsystem dynamic model and of an adaptive approximation of the uncertain interconnection with neighboring subsystems. The goal is to integrate all the aspects of the monitoring process in a comprehensive architecture, taking into account the physical environment, the sensor layer, the diagnosers level and the communication networks. In particular, specifically designed methods are developed in order to take into account the issues emerging when dealing with communication networks and distributed systems. The introduction of the sensor layer, composed by a set of sensor networks, allows the decoupling of the physical and the sensing/computation topologies, bringing some advantages, such as scalability and reliability of the diagnosis architecture. We design the measurements acquisition task by proposing a distributed estimation method for sensor networks, able to filter measurements so that both the variance and the mean of the estimation error are minimized by means of a Pareto optimization problem. Moreover, we consider multi-rate systems and non synchronized measurements, having in mind realistic applications. A re-synchronization method is proposed in order to manage the case of multi-rate systems and to compensate delays in the communication network between sensors and diagnosers. Since one of the problems when dealing with distributed, large-scale or networked systems and therefore with a communication network, is inevitably the presence of stochastic delays and packet dropouts, we propose therefore a distributed delay compensation strategy in the communication network between diagnosers, based on the use of Time Stamps and buffers and the definition of a time-varying consensus matrix. The goal of the novel time-varying matrix is twofold: it allows to manage communication delays, packet dropouts and interrupted links and to optimize detectability and isolability skills by defining less conservative thresholds. The distributed fault detection and isolation schemes are studied and analytical results regarding fault detectability, isolability and estimator convergence are derived. Simulation results show the effectiveness of the proposed architecture. For the sake of completeness, the monitoring architecture is studied and adapted to different frameworks: the fault detection and isolation methodology is extended for continuous-time systems and the case where the state is only partially measurable is considered for discrete-time and continuous-time systems.XXV Ciclo198

Internationales Kolloquium über Anwendungen der Informatik und Mathematik in Architektur und Bauwesen : 20. bis 22.7. 2015, Bauhaus-Universität Weimar

The 20th International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering will be held at the Bauhaus University Weimar from 20th till 22nd July 2015. Architects, computer scientists, mathematicians, and engineers from all over the world will meet in Weimar for an interdisciplinary exchange of experiences, to report on their results in research, development and practice and to discuss. The conference covers a broad range of research areas: numerical analysis, function theoretic methods, partial differential equations, continuum mechanics, engineering applications, coupled problems, computer sciences, and related topics. Several plenary lectures in aforementioned areas will take place during the conference. We invite architects, engineers, designers, computer scientists, mathematicians, planners, project managers, and software developers from business, science and research to participate in the conference