Fault-tolerant Stochastic Distributed Systems

The present doctoral thesis discusses the design of fault-tolerant distributed systems, placing emphasis in addressing the case where the actions of the nodes or their interactions are stochastic. The main objective is to detect and identify faults to improve the resilience of distributed systems to crash-type faults, as well as detecting the presence of malicious nodes in pursuit of exploiting the network. The proposed analysis considers malicious agents and computational solutions to detect faults. Crash-type faults, where the affected component ceases to perform its task, are tackled in this thesis by introducing stochastic decisions in deterministic distributed algorithms. Prime importance is placed on providing guarantees and rates of convergence for the steady-state solution. The scenarios of a social network (state-dependent example) and consensus (time- dependent example) are addressed, proving convergence. The proposed algorithms are capable of dealing with packet drops, delays, medium access competition, and, in particular, nodes failing and/or losing network connectivity. The concept of Set-Valued Observers (SVOs) is used as a tool to detect faults in a worst-case scenario, i.e., when a malicious agent can select the most unfavorable sequence of communi- cations and inject a signal of arbitrary magnitude. For other types of faults, it is introduced the concept of Stochastic Set-Valued Observers (SSVOs) which produce a confidence set where the state is known to belong with at least a pre-specified probability. It is shown how, for an algorithm of consensus, it is possible to exploit the structure of the problem to reduce the computational complexity of the solution. The main result allows discarding interactions in the model that do not contribute to the produced estimates. The main drawback of using classical SVOs for fault detection is their computational burden. By resorting to a left-coprime factorization for Linear Parameter-Varying (LPV) systems, it is shown how to reduce the computational complexity. By appropriately selecting the factorization, it is possible to consider detectable systems (i.e., unobservable systems where the unobservable component is stable). Such a result plays a key role in the domain of Cyber-Physical Systems (CPSs). These techniques are complemented with Event- and Self-triggered sampling strategies that enable fewer sensor updates. Moreover, the same triggering mechanisms can be used to make decisions of when to run the SVO routine or resort to over-approximations that temporarily compromise accuracy to gain in performance but maintaining the convergence characteristics of the set-valued estimates. A less stringent requirement for network resources that is vital to guarantee the applicability of SVO-based fault detection in the domain of Networked Control Systems (NCSs)

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Kernel-based fault diagnosis of inertial sensors using analytical redundancy

Kernel methods are able to exploit high-dimensional spaces for representational advantage, while only operating implicitly in such spaces, thus incurring none of the computational cost of doing so. They appear to have the potential to advance the state of the art in control and signal processing applications and are increasingly seeing adoption across these domains. Applications of kernel methods to fault detection and isolation (FDI) have been reported, but few in aerospace research, though they offer a promising way to perform or enhance fault detection. It is mostly in process monitoring, in the chemical processing industry for example, that these techniques have found broader application. This research work explores the use of kernel-based solutions in model-based fault diagnosis for aerospace systems. Specifically, it investigates the application of these techniques to the detection and isolation of IMU/INS sensor faults – a canonical open problem in the aerospace field. Kernel PCA, a kernelised non-linear extension of the well-known principal component analysis (PCA) algorithm, is implemented to tackle IMU fault monitoring. An isolation scheme is extrapolated based on the strong duality known to exist between probably the most widely practiced method of FDI in the aerospace domain – the parity space technique – and linear principal component analysis. The algorithm, termed partial kernel PCA, benefits from the isolation properties of the parity space method as well as the non-linear approximation ability of kernel PCA. Further, a number of unscented non-linear filters for FDI are implemented, equipped with data-driven transition models based on Gaussian processes - a non-parametric Bayesian kernel method. A distributed estimation architecture is proposed, which besides fault diagnosis can contemporaneously perform sensor fusion. It also allows for decoupling faulty sensors from the navigation solution

A Novel Case of Practical Exponential Observer Using Extended Kalman Filter

This technical note presents a case of practical exponential observer using extended Kalman filter (EKF) independent of certain restrictions, such as online check and estimation error of initial state. Recursive state estimation is usually a challenge for discrete-time nonlinear system in terms of computation cost. EKF is attractive with its simplicity since it is considered as an exponential observer given the above restrictions. However, those restrictions are so mathematically complicated that EKF cannot be practical in estimation. A novel case for an exponential observer using EKF is proposed, which is independent of such restrictions. However, these restrictions are proved to be unnecessary in the case. The proposed case is illustrated by a navigation system scenario. The validity of the case is demonstrated by a numerical simulation experiment. The system is deterministic

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

Model based fault detection and isolation approach for actuator and sensor faults in a UAV

Thesis (MEng)--Stellenbosch University, 2021.ENGLISH ABSTRACT: This thesis presents the design and validation of model-based fault detection and isolation (FDI) approach for unmanned aerial vehicles (UAV). In safety-critical sys- tems such as chemical, nuclear plants and passenger aircraft, FDI is typically founded on hardware redundancy. In hardware redundancy, multiple actuators are spatially distributed to localise faults quickly, and sensor measurements are compared for consistency. The primary drawback with hardware redundancy is the increased installation complexity, weight, and costs. With modern computing technologies, model-based FDI offers a cost-effective, iterative and efficient FDI design process, verifiable with high fidelity computer-aided simulation (CAS). This thesis investigates the application of the Two-Stage Kalman filter (TSKF) to the problem of FDI. The TSKF solves the main deficiencies faced with the aug- mented state Kalman filter (ASKF), namely, numerical instability in ill-conditioned systems, and computational inefficiency where large parameter vectors are aug- mented. The TSKF approach utilises two parallel reduced-order KFs to estimate the system state and the parameter vectors separately. The UAV’s two rudders are not "isolable" because they produce identical moments. A novel active FDI (AFDI) method is proposed to isolate rudder actuator faults. The FDI displays high noise sensitivity under the evere Dryden turbulence model, resulting in high false detection and missed detection rates. A novel adap- tive technique is proposed to improve the robustness and sensitivity of the FDI. Unlike most methods which rely on a single scaling factor, the proposed adaptation technique employs multiple factors to weight the spread of fault parameter covari- ance matrix in the direction of flow of information, resulting in selective adaptation. Fault parameter variations are nonuniform in time and space. A static alarm threshold will induce high false alarms or missed alarms when set to low or too high, respectively. A novel adaptive threshold based on the normalised innovation squared (NIS) is proposed. A Monte Carlo campaign is carried out to validate the FDI while fault-sizes, the aircraft’s physical parameters, and disturbances are scat- tered, each with a distinct mean dispersion. The proposed strategy exhibits high robustness to noise and sensitivity to faults which indicates a reliable FDI.AFRIKAANSE OPSOMMING: Hierdie tesis beskryf die ontwerp en validering van ‘n model-gebaseerde foutop- sporing en isolasie (“fault deteciton and isolation (FDI)”) tegniek vir onbemande lugvoertuie (“unmanned aerial vehicles (UAVs)”). In veiligheidskritieke stelsels soos chemiese aanlegte, kernkragaanlegte, en passasiersvliegtuie, word FDI gewoon- lik gebaseer op hardeware-oortolligheid. Vir hardeware-oortolligheid word verskeie aktueerders ruimtelik versprei om foute vinnig op te spoor, en sensormetings word vergelyk vir ooreenstemming. Die primêre nadeel van hardeware-oortolligheid is die verhoogde installasie-kompleksiteit, gewig en koste. Met moderne rekenaarteg- nologieë bied model-gebaseerde FDI ’n koste-effektiewe, iteratiewe en doeltref-fende FDI-ontwerpproses met ‘n hoë betroubaarheid wat bevestig kan word met rekenaargesteunde simulasie. Hierdie tesis ondersoek die toepassing van die twee-stadium Kalman filter (“two- stage Kalman filter (TSKF)”) op die probleem van FDI. Die TSKF los die belangrik- ste tekortkominge van die uitgebredie-toestand Kalman-filter (“augmented state Kalman filter (ASKF)”) op, naamlik numeriese onstabiliteit in swak gekondisioneerde stelsels, en berekeningsondoeltreffendheid waar groot parametervektore bygevoeg word. Die TSKF-benadering gebruik twee parallelle Kalman filters met vermin- derde orde om die stelseltoestand en die parametervektore afsonderlik af te skat. Die UAV se twee roere (“rudders”) is egter nie “isoleerbaar” nie omdat dit hulle identiese draaimoment veroorsaak. ’n Nuwe aktiewe FDI-metode (AFDI) word voorgestel om die roeraktueerderfoute te isoleer. Die FDI vertoon hoë sensitiwiteit vir geraas vanaf erge turbulensie soos gemod- elleer deur die Dryden-turbulensie-model, wat lei tot ‘n groot aantal vals deteksies en gemiste deteksies. ’n Nuwe aanpassingstegniek word voorgestel om die robu- ustheid en sensitiwiteit van die FDI te verbeter. Anders as die meeste metodes wat op een enkele skaalfaktor staatmaak, gebruik die voorgestelde aanpassingstegniek verskeie faktore om die verspreiding van die foutparameterkovariansiematriks in die rigting van informasievloei te weeg, wat lei tot selektiewe aanpassing. Foutparametervariasies is nie eenvormig in tyd of ruimte nie. ’n Statiese alar- mdrempel sal hoë vals deteksies of gemiste deteksies veroorsaak as dit onderskei-delik óf te laag óf te hoog gestel is. ’n Nuwe aanpassingsdrempel wat gebaseer is op die genormaliseerde innovasie kwadraat (NIS) word voorgestel. ’n Monte Carlo simulasieveldtog is uitgevoer om die FDI te toets met die foutgroottes, die fisiese parameters van die vliegtuig, en die steurings lukraak gevarieer elk met ’n duide- like gemiddelde verspreiding. Die voorgestelde strategie vertoon ’n hoë robuus- theid vir geraas en sensitiwiteit vir foute, wat dui op ’n betroubare FDI