Fault Isolation

The fault-tolerant control problem belongs to the domain of complex control systems in which inter-control-disciplinary information and expertise are required. This paper outlines the state of the art in a field which remains largely a theoretical topic with most application studies based upon aerospace systems. The directions in which the subject is going are summarised and some pointers are given as to the likely future issues and where new research effort is required. The paper provides a basic literature review covering most areas of fault-tolerant control.

This paper develops a fault diagnosis (FD) and fault-tolerant control (FTC) of pitch actuators in wind turbines. This is accomplished by combining a disturbance compensator with a controller, both of which are formulated in the discrete time domain. The disturbance compensator has a dual purpose: to estimate the actuator fault (which is used by the FD algorithm) and to design the discrete time controller to obtain an FTC. That is, the pitch actuator faults are estimated, and then, the pitch control laws are appropriately modified to achieve an FTC with a comparable behavior to the fault-free case. The performance of the FD and FTC schemes is tested in simulations with the aero-elastic code FAST.

When dealing with dynamic, untrusted content, such as on the Web, software behavior must be sandboxed, typically through use of a language like JavaScript. However, even for such speciallydesigned languages, it is difficult to ensure the safety of highlyoptimized, dynamic language runtimes which, for efficiency, rely on advanced techniques such as Just-In-Time (JIT) compilation, large libraries of native-code support routines, and intricate mechanisms for multi-threading and garbage collection. Each new runtime provides a new potential attack surface and this security risk raises a barrier to the adoption of new languages for creating untrusted content. Removing this limitation, this paper introduces general mechanisms for safely and efficiently sandboxing software, such as dynamic language runtimes, that make use of advanced, lowlevel techniques like runtime code modification. Our languageindependent sandboxing builds on Software-based Fault Isolation (SFI), a traditionally static technique. We provide a more flexible form of SFI by adding new constraints and mechanisms that allow safety to be guaranteed despite runtime code modifications. We have added our extensions to both the x86-32 and x86-64 variants of a production-quality, SFI-based sandboxing platform; on those two architectures SFI mechanisms face different challenges. We have also ported two representative language platforms to our extended sandbox: the Mono common language runtime and the V8 JavaScript engine. In detailed evaluations, we find that sandboxing slowdown varies between different benchmarks, languages, and hardware platforms. Overheads are generally moderate and they are close to zero for some important benchmark/platform combinations.

Software Fault Isolation (SFI) is an effective approach to sandboxing binary code of questionable provenance, an interesting use case for native plugins in a Web browser. We present software fault isolation schemes for ARM and x86-64 that provide control-flow and memory integrity with average performance overhead of under 5% on ARM and 7% on x86-64. We believe these are the best known SFI implementations for these architectures, with significantly lower overhead than previous systems for similar architectures. Our experience suggests that these SFI implementations benefit from instruction-level parallelism, and have particularly small impact for workloads that are data memory-bound, both properties that tend to reduce the impact of our SFI systems for future CPU implementations.

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This paper considers the robust fault detection and isolation (FDI) problem for linear time-invariant dynamic systems subject to faults, disturbances and polytopic uncertainties. We employ an observer-based FDI filter to generate a residual signal. We propose a cost function that penalizes a weighted combination of the deviation of the fault to residual dynamics from a given fault isolation reference model, as well as the effects of disturbances and uncertainties on the residual, using the H∞ norm as a measure. The proposed cost function thus captures the requirements of fault detection and isolation and disturbance rejection in the presence of polytopic uncertainties. We derive necessary and sufficient conditions for the existence of an FDI filter that achieves the design specifications. This condition takes the form of easily implementable linear matrix inequality (LMI) optimization problem.

In this paper the application of the concepts of failure detection, fault diagnosis and dynamic reconfiguration to real time aircraft flight control systems is reviewed in detail. Some new results concerning the sensitivity and insensitivity of observers and a new approach to reconfiguration of control laws are also presented.

Inference Systems (ANFIS), are implemented to diagnose the leakage faults in a nonlinear three tank system. Two separate structures are utilized for fault diagnosis. One is to identify the dynamics of the plant and the other is to construct the residual logic mechanism. The performance of the proposed methods are evaluated by simulations carried out on a three tank system (TTS). The leakages in tanks are considered as faults in the tank system.

The heart of a Blue Gene A /Q system is the Blue Gene/Q Compute (BQC) chip, which combines processors, memory, and communication functions on a single chip. The Blue Gene/Q Compute chip has 16 þ 1 þ 1 processor cores, each with a quad single-instruction, multiple-data (SIMD) floating-point unit, and a multi-versioned Level 2 cache that provides hardware support for transactional memory, speculative execution, and atomic operations. The Blue Gene/Q Compute chip further contains dual on-chip memory controllers for directly attached DDR3 (double data rate type 3) memory and sophisticated networking logic for chip-to-chip communications.

The proposed fault diagnosis scheme relies on a residual generation based on an adaptive observer covering linear time varying (LTV), linear parameter varying (LPV), and state-affine non-linear systems, all with bounded uncertainties. A residual evaluation is then performed by set-membership computations based on zonotopes (polytopes defined as the image of a hypercube by a linear application). The main advantage of the approach is its rigorous computation of the propagation of pre-specified modelling uncertainty bounds. Within the assumed uncertainty bounds, fault detection is guaranteed to be free of false alarm, while not being too much conservative, as illustrated on the model of a satellite.

The diagnosis of a system lies on fault detection and localization, but also on magnitudes estimation of the detected faults. A method widely used for this diagnosis is Principal Component Analysis (PCA). After detection, the faults are frequently localized using the technique of structuring residues. It is to find a modification that makes the transformed residues sensitive or insensitive to certain faults. The amplitude of the detected and localized fault is then estimated using a variables reconstruction technique. The latter relies on estimating a variable from the PCA model and other available variables [1, 2].

A diagnosis strategy based on fuzzy reasoning is presented in this paper. The first stage of the suggested approach consists in generating residuals revealing of the process functioning state. Two methods of residuals generation for linear-dynamic systems, based on the exploitation of analytical redundancy, are proposed. Then, a procedure of fault isolation using these residuals and based on fuzzy concepts is explained and implemented. To illustrate and show the validity of this approach, it's applied to experimental signals collected on a urban water supply network.

This paper presents a Fault Detection and Isolation (FDI) method based on a fuzzy evaluation of residuals. The advantages of this non-Boolean evaluation are described. The ability of this approach to integrate a persistence index of the residual deviations and the sensitivity of the residuals with regard to faults is particularly studied. Some results issued from the implementation of the proposed approach on an urban water supply network illustrate its pertinence.

In this paper, we show how the generation of parity equations technique can be extended to a singular system. The decomposition of a singular system into two subsystems, a slow and a fast subsystems, allows us to generalize the technique used for standard systems. For an observation horizon with finished length, the system output can be particularly expressed in terms of the initial and terminal state and the inputs; then, the elimination of these two particular states provides the desired parity equations which can be used for diagnosis. adaptation of control law to reduce failure effects and repairing of defective components. To detect anomalies, one approach consists in using a process model to control the process behaviour. In this way, we use the analytical redundancy which connects the process inputs and outputs owing to the model. If this latter is correct (i.e. if it provides a reliable image of process behavior), we can check the appropriateness of measurements carried out on the process with the model; a non-null deviation can be a result of component failure of the physical system, or of actuator and/or sensor failures.

This article tackles the problem of the diagnosis of switching systems. These systems are described by several regimes, each of them being active under certain particular operating conditions which can be controlled or not. If the operating conditions are unknown, the diagnosis of such systems is complex. It is even more complicated in the situation where the various models of the operating regimes of the switching system tend to give very close outputs when they are excited with the same inputs. Moreover the ...

Abstract. When modelling a physical process, there are always discreapencies between the modelled and real behaviours due to simplifications and neglected effects. Moreover, the parameters of the real process are never rigorously constant and may vary all along the time around a mean value. Thus the process state estimation will strongly depend on these variations which are unknown and thus may be considered as uncertainties. In order to obtain a guaranteed state reconstruction, the influence of these uncertainties have to be ...

In this paper a new approach for increasing the overall reliability of a monitoring and decision support system will be explained. The focus is on systems used for ship operator guidance with respect to, say, speed and heading. The basic idea is to convert the given system into a fault tolerant system and to improve multi-sensor data fusion for the particular system. Fault isolation is an important part of the fault tolerant design for in-service monitoring and decision support systems for ships. In the paper, a virtual example of fault isolation will be presented. Several possible faults will be simulated and isolated using residuals and the generalized likelihood ratio (GLR) algorithm. It will be demonstrated that the approach can be used to increase accuracy of sea state estimations employing sensor fusion quality test.

Electro-mechanical actuators (EMA) are finding increasing use in aerospace applications, especially with the trend towards all all-electric aircraft and spacecraft designs. However, electro-mechanical actuators still lack the knowledge base accumulated for other fielded actuator types, particularly with regard to fault detection and characterization. This paper presents a thorough analysis of some of the critical failure modes documented for EMAs and describes experiments conducted on detecting and isolating a subset of them. The list of failures has been prepared through an extensive Failure Modes and Criticality Analysis (FMECA) reference, literature review, and accessible industry experience. Methods for data acquisition and validation of algorithms on EMA test stands are described. A variety of condition indicators were developed that enabled detection, identification, and isolation among the various fault modes. A diagnostic algorithm based on an artificial neural network is shown to operate successfully using these condition indicators and furthermore, robustness of these diagnostic routines to sensor faults is demonstrated by showing their ability to distinguish between them and component failures. The paper concludes with a roadmap leading from this effort towards developing successful prognostic algorithms for electromechanical actuators. 1 2

This paper describes an architecture and elements for an integrated prognostic bearings reasoner. The goal of the reasoner is to arrive at a reliable measure of damage accumulation, quantify its confidence, and aid in the assessment of remaining life. The reasoning integrates inflight and post-flight functions. During flight, the tasks are primarily diagnostic and assess damage in real time using input from a plurality of sources, both sensor-based and model-based. The damage assessment is further refined after conclusion of the flight with full-order models and additional information from historical failure data, operational data, and inspection data. Load profiles from future missions are used to calculate the damage propagation which will allow the reasoner to assess remaining life. This paper will lay out the overall process and then focus in more detail on the in-flight reasoner. The operation of the architecture is demonstrated for bearing prognosis via an illustrative example. TABLE OF CONTENTS 1. INTRODUCTION.

Two fundamentally different approaches can be employed to estimate remaining life in faulted components. One is to model from first principles the physics of fault initiation and propagation. Such a model must include detailed knowledge of material properties, thermodynamic and mechanical response to loading, and the mechanisms for damage creation and growth. Alternatively, an empirical model of condition-based fault propagation rate can be developed using data from experiments in which the conditions are controlled or otherwise known and the component damage level is carefully measured. These two approaches have competing advantages and disadvantages. However, fusing the results of the two approaches produces a result that is more robust than either approach alone. In this paper, we introduce an approach to fuse competing prediction algorithms for prognostics. Results presented are derived from rig test data wherein multiple bearings were first seeded with small defects, then exposed to a variety of speed and load conditions similar to those encountered in aircraft engines, and run until the ensuing material liberation accumulated to a predetermined damage threshold or cage failure, whichever occurred first.

In this paper a novel distributed adaptive dual-layer super-twisting sliding mode observer-based scheme is designed to isolate, reconstruct and mitigate the effects of disturbances and a class of communication attacks affecting both generator nodes and load nodes in power networks. Voltage phase angles are measured at each node by means of Phasor Measurement Units (PMUs). Based on this information, an interconnection of adaptive dual-layer supertwisting sliding mode observers is designed both to estimate frequency deviation in each generator node, and to perform robust detection and reconstruction of both disturbances and a class of communication attacks. The proposed estimation scheme exhibits a distributed structure, since it requires only information received from neighbouring nodes and measurements taken locally in the power network. The novelty of the proposed scheme is its capability to reconstruct simultaneous disturbances affecting the generator nodes and load nodes, automatically adjusting the values of the gains of the observers. More precisely, the adaptive gains of the observer obey a recently proposed dual-layer adaptation law for the super-twisting sliding mode architecture. A disturbance mitigation strategy is also proposed at each generator node utilising the disturbance estimates. Numerical simulations are discussed to assess the proposed distributed scheme.

An algorithm is proposed for computing which sensor additions that make a diagnosis requirement specification regarding fault detectability and isolability attainable for a given linear differential-algebraic model. Restrictions on possible sensor locations can be given and if the diagnosis specification is not attainable with any available sensor addition, the algorithm provides the solutions that maximize specification fulfillment. Previous approaches with similar objectives have been structural, but since this algorithm is analytical, it can handle models where structural approaches fail. A Mathematica implementation of the algorithm can be downloaded from .

A Fault Detection and Identification (FDI) scheme for aircraft systems based on the modelling of relationships among flight variables is introduced. The modelling is performed by means of Time-dependent Functionally Pooled Nonlinear AutoRegressive with eXogenous (TFP-NARX) excitation representations. These are generalized NARX representations with (a) their parameters being functions of time-dependent flight variables and (b) the capability of describing a system under various operating conditions due to their pooled form. During the system's operation in healthy mode, these relationships are valid. Hence a scheme using statistical hypothesis testing is designed to detect changes in the relationships due to potential fault occurrence. The FDI scheme's performance and robustness are assessed with flights conducted under various flight conditions.

The Blue Genet/L computer is a massively parallel supercomputer based on IBM system-on-a-chip technology. It is designed to scale to 65,536 dual-processor nodes, with a peak performance of 360 teraflops. This paper describes the project objectives and provides an overview of the system architecture that resulted. We discuss our application-based approach and rationale for a low-power, highly integrated design. The key architectural features of Blue Gene/L are introduced in this paper: the link chip component and five Blue Gene/L networks, the PowerPCt 440 core and floatingpoint enhancements, the on-chip and off-chip distributed memory system, the node-and system-level design for high reliability, and the comprehensive approach to fault isolation.

State reconstruction approach is very useful for sensor fault isolation, reconstruction of faulty measurement and the determination of the number of components retained in the principal components analysis (PCA) model. An extension of this approach based on a Nonlinear PCA (NLPCA) model is described in this paper. The NLPCA model is obtained using five layer neural network. A simulation example is given to show the performances of the proposed approach.

Abstract: Recently, fault detection and process monitoring using principal component analysis (PCA) were studied intensively and largely applied to industrial process. PCA is the optimal linear transformation with respect to minimizing the mean squared prediction error. If the data have nonlinear dependencies, an important issue is to develop a technique which can take into account this kind of dependencies. Recognizing the shortcomings of PCA, a nonlinear extension of PCA is developed. This paper proposes an application for sensor ...

This paper presents a multi-layer scheme to control a formation of n mobile robots, including a strategy for obstacle avoidance. The controller adopted is able to guide the robots to track the desired trajectory, avoiding obstacles during the navigation. Stability analysis performed for the closed-loop system shows that the formation errors are ultimately bounded. Finally, simulation results for a group of four unicycle-like mobile robots are presented, showing the viability of using the proposed scheme to guarantee a safe navigation of the formation.

In the paper a systematic view of the shunt circuit breaker technology as a relatively new concept of distribution system earthing is given. This technology is primarily intended to improve distribution system power quality by creating the conditions for the elimination of transient single pole-to-earth faults without the interruption of power supply. The paper starts with the explanation of the shunt circuit breaker operation. Additionally, a brief description of the faulted phase detection principle and the coordination with earth fault protection is given. Then the paper moves in the development of a mathematical model based on the theory of symmetrical components intended for the calculation of the fault current on the faulted point and shunt circuit breaker point when the shunt circuit breaker is activated. The model is then applied on a test distribution network with different basic earthings for the purpose of the analysis how the network parameters, fault location, basic ear...

An intelligent transmitter reliability study has to deal with several issues: various interactions between both material elements and functions; behaviors of components as programmable units and software which are difficult to predict when faults or failures occur, as well as the consequences on functions processing. A “3-step model” is therefore proposed to include both functional and material aspects, using Goal Tree–Success Tree (GTST), and setting faults and failures as a third full part. Then, Master Logic Diagrams (MLD) aim to represent several types of relationships between faults or failures, material elements, and functions. Probabilities are used for MLD components to take the indeterminate relationships into account. Quantitative assessments are then performed, using an infrared gas transmitter as an example: total relationships between any fault or failure and any function, probabilities of malfunction and failure modes. Moreover, uncertainty analyses show that even if i...

The method used for neutral point grounding is very important for power network operation. There are many ways of grounding, which are used in practice, and the decision on the method of neutral grounding depends on the situation in the network connected to the substation. When deciding on the method of neutral grounding it is necessary to thoroughly consider all the advantages and disadvantages of individual modes of neutral grounding, and then choose the best technical and economical solution.

This paper presents a diagnostic methodology relying on a set-membership approach for fault detection and on a causal model for fault isolation. Set-membership methods are a promising approach to fault detection because they take into account a priori knowledge of model uncertainties and measurement errors. Every uncertain model parameter and/or measurement is represented by a bounded variable. In this paper, detection consists of verifying the membership of measurements to an interval. First order discrete time models are used and their output is explicitly computed with interval arithmetic. Fault isolation relies on a causal analysis and the exoneration principle, which allows focusing the consistency tests on simple local models. The isolation strategy consists of two steps: performing minimal tests found with the causal graph and determining on line additional relevant tests that reduce the final diagnosis. An application for a nuclear process is used in order to illustrate the method's efficiency.

This paper proposes an approach for the joint state and fault estimation for a class of uncertain nonlinear systems with simultaneous unknown input and actuator faults. This is achieved by designing an unknown input observer combined with a set-membership estimation in the presence of disturbances and measurement noise. The observer is designed using quadratic boundedness approach that is used to overbound the estimation error. Sufficient conditions for the existence and stability of the proposed state and actuator fault estimator are expressed in the form of linear matrix inequalities (LMIs). Simulation results for a quadruple-tank system show the effectiveness of the proposed approach.

In Very-Large-Scale-Integration (VLSI) designs, thorough testing is indispensable for identifying the structural defects of the chip. Timely detection and correcting serious defects are pivotal in preventing faulty chips from reaching customers and avoiding failures. In scan designs, the toggling activity is a critical factor due to the defects between lower cells and metal layers, exacerbated by diverse Process, Voltage, and Temperature (PVT) conditions. These defects significantly impact design testability, quality, and reliability, necessitating meticulous testing to ensure that chips meet the desired specifications. Effectively managing power consumption in the current VLSI landscape is crucial amid the ongoing energy crisis. Balancing the need for Low-Power (LP) with the complexity of integrating transistors onto a single silicon substrate poses significant challenges. As chip densities increase, power dissipation during testing surges, adversely affecting durability, performance, cost, and reliability. Engineers are racing to optimize test power usage, employing advanced Design for Test (DFT) techniques to incorporate efficient power management into Silicon-on-Chip (SoC) designs. Linear Feedback Shift Registers (LFSRs) are phenomenal in addressing DFT parameters like power, and performance, and for better pseudo-random pattern generation. Hence, this paper proposes a groundbreaking approach to power reduction techniques deploying the LFSR architecture, and thus challenging conventional scan-based testing methods. The proposed LFSR architecture is meticulously designed and rigorously tested using Cadence DFT Modus solution on ISCAS'89 benchmarking circuits. Coverage was unequivocally evaluated for Quality of Results (QoR) metrics, such as fault coverage, memory usage, pattern counts, switching activity, fault testing, and runtime. The specific evaluation clearly proved the superiority of the LFSR-based approach over the scanbased architecture. Adopting the novel LFSR architecture resulted in a ~5.6X reduction in toggling activity accompanied by a substantial ~10K pattern reduction and a runtime of nearly ~1.5 hours. Notably, the test power was reduced to 50% showcasing superior efficiency. This approach emerges as the ideal solution for industrial designs providing the best QoR for power, performance, and area. This innovative methodology marks a significant leap toward an energy-efficient and cost-effective VLSI circuit especially for stacked 2.5-3D ICs and chiplets poised to revolutionize chip manufacturing.

This paper considers the robust fault detection and isolation (FDI) problem for linear time-invariant dynamic systems subject to faults, disturbances and polytopic uncertainties. We employ an observer-based FDI filter to generate a residual signal. We propose a cost function that penalizes a weighted combination of the deviation of the fault to residual dynamics from a given fault isolation reference model, as well as the effects of disturbances and uncertainties on the residual, using the H∞ norm as a measure. The proposed cost function thus captures the requirements of fault detection and isolation and disturbance rejection in the presence of polytopic uncertainties. We derive necessary and sufficient conditions for the existence of an FDI filter that achieves the design specifications. This condition takes the form of easily implementable linear matrix inequality (LMI) optimization problem.

This paper addresses fault detection and isolation (FDI) problem using a sliding mode fuzzy observer on the basis of a uncertain Takagi-Sugeno (T-S) fuzzy model. First, a robust fuzzy observer with respect to the uncertainties is designed. The convergence of the fuzzy observer is performed by the search of suitable Lyapunov matrices. It is shown how to synthesis observers using a set of linear matrix inequalities (LMI) conditions. Once the fuzzy observer is designed, FDI problem for nonlinear systems described by T-S fuzzy systems using the fuzzy observer is presented. A bank of fuzzy observer is then designed in order to investigate fault diagnosis problems. The validity of the proposed methodology is illustrated on a dynamic vehicle model.

Strengthening the ownership rights on outsourced relational database is very important in today's internet environment. Especially where sensitive, valuable content is to be outsourced. Let us take an example of university database, weather data, stock market data, power consumption consumer behavior data, and medical and scientific data. The increasing use of databases in applications beyond "behind-the-firewalls data processing" is creating a need for watermarking relational databases. Watermarking for relational data is made possible by fact that real data can very often tolerate a small amount of errors without any significant degradation with respect to their usability. In this paper, we present a mechanism that will overcome the additive attacks, how to embed and detect watermark in relational database. In additive attack the attacker simply inserts his/her own watermark in original data. In our proposed system we can restrict the additive attack by embedding multiple watermarks in original data. In multiple watermarking the owner of the data first inserts the watermarks and again applies the watermarks by choosing the function g which restricts the percentage of watermarks. If attacker inserts very small amount of watermark the data goes useless.

This paper discusses the design and implementation of an integrated diagnosis system, MDS (Multi-level Diagnosis System), which combines associational and model-based approaches to diagnosis. The design and implementation of the associational module is tailored to achieving efficiency in routine diagnostic problem solving, and to providing a desirable interface for the users. The model-based diagnosis module is developed to achieve completeness

'Diagnosis of dynamic physical systems is complex and requires close interaction of monitoring, fault gen-eration and refinement, and prediction. We establish a methodology for model-based diagnosis of continuous systems in a qualitative reasoning framework. A tem-poral causal model capturing dynamic system behavior identifies faults from deviant measurements and predicts future system behavior expressed as signatures, ie, qualitative magnitude changes and higher order time-derivative effects. A comparison of the transient ...

This paper develops a fault diagnosis methodology for civil engineering structures based on the bond graph approach. The bond graph theory provides a modeling framework that includes parametric models of the physical system and the sensors. Structural faults are modeled as abrupt or gradual damage in structural components. Sensor faults are modeled as biases or drifts from true measurements. Fault detection uses a statistical method to identify significant deviations of measurements from nominal behavior of the structure. ...

Multiple fault diagnosis is a challenging problem because the number of candidates grows exponentially in the number of faults. In addition, multiple faults in dynamic systems may be hard to detect, because they can mask or compensate each other's effects. The multiple fault problem is important, since the single fault assumption can lead to incorrect or failed diagnoses when multiple faults occur. We present an approach to simultaneous and cascaded multiple fault diagnosis in dynamical systems. Our approach is based on the ...

Fault diagnosis is essential for guaranteeing safe and reliable operation of complex engineering systems. Our work focuses on diagnosis of parametric faults in the components of dynamic systems, whose temporal profile can be categorized as incipient (slow) or abrupt (fast). The diagnosis of abrupt and incipient faults using qualitative approaches is challenging, since in many situations, these faults produce similar qualitative effects. Quantitative estimation methods may provide more discriminatory power, but these approaches can be computationally infeasible for large systems with nonlinearities and complex dynamics. In this paper, we combine a qualitative fault isolation scheme with an Dynamic Bayes net-based particle filtering approach for the comprehensive diagnosis of incipient and abrupt faults in continuous systems. We also present experimental results to demonstrate the effectiveness of our approach when applied to a two-tank system.

This paper reports on the findings of an on-going project to investigate techniques to diagnose complex dynamical systems that are modeled as hybrid systems. In particular, we examine continuous systems with embedded supervisory controllers which experience abrupt, partial or full failure of component devices. The problem we address is: given a hybrid model of system behavior, a history of executed controller actions, and a history of observations, including an observation of behavior that is aberrant relative to the model of ...

Electromechanical Actuators (EMAs) are increasingly being considered for safety critical applications across the aerospace sector because of the potential benefits this technology can offer compared to traditional types of actuator. One of the main challenges for EMA implementation within safety critical applications is mitigating the single point of failure: ballscrew jamming. Better understanding of the onset of EMA ballscrew jamming, and consequences of jamming, would make EMAs a more viable option for aerospace safety critical applications. Through Hierarchical Process Modelling (HPM) and review of previous literature, various diverse strategies were considered to mitigate EMA ballscrew jamming. The most favoured approach was fault diagnostics i.e. predicting the state of health of a component or onset of failure by monitoring system operation. Monitoring the state of health of a component in turn allows new maintenance strategies, for example Condition Based Maintenance (CBM). As part of a systems approach towards evaluating fault diagnostics, a Systems Dynamics (SD) study was conducted to capture the cost benefits of applying a CBM maintenance strategy to a fleet of aircraft in comparison to a conventional time-based maintenance strategy. Historical in-service data for EMAs was used in this study and it was notable that the reliability of EMAs had significantly improved over the timescale of the data, which meant the direct economic benefit of CBM was reduced. Therefore, the primary benefit of fault diagnostics was improving safety. Sensing for fault detection was limited to using motor current alone, thus exploiting an existing sensor of the actuator system. A hybrid approach to fault diagnostics was proposed which utilises a combination of high-fidelity modelling of the system and experimental data to supplement the real application being monitored. Through simulations it was demonstrated that feature extraction of ballscrew dynamic frictional behaviour could be achieved through Iq current analysis. The results were also mapped to health states in a classification algorithm. The analysis was then extended to an industrial based case study for an Airbus A320 Nose Landing Gear extension-retraction system where it was demonstrated that EMA ballscrew stiction could be identified through motor current in the presence of aero loads. The final outcome of this thesis was the development of a CBM technique for the A320 NLG extensionretraction system. The approach taken was to perform offline CBM checks on accumulated flight data (motor current signals) on a weekly basis. The diagnostics were based on extracting dynamic friction features of the EMA ballscrew through Iq currents and comparison of these with an analytical model in a hybrid approach. v0 Velocity Range for Stribeck Effect

Performance engineering aims to meet a system's resource constraints which implies to make the code as lean as possible. Robustness engineering has to support fault identification and fault recovery. To make a system sufficiently robust, code needs to be added. Hence, robustness engineering is in conflict with a system's performance. This paper will discuss this potential conflict in the context of operating systems. Operating systems are significantly impacting performance and robustness properties according to what they are providing or do not support. Current practices and related impacts are identified and recommendations are given how this conflict can be minimised.

This paper proposes a sensor-fault detection and isolation (FDI) approach based on interval observers and invariant sets. In fault detection (FD), both interval observer-based and invariant set-based mechanisms are used to provide real-time fault alarms. In fault isolation (FI), the proposed approach also uses these two different mechanisms. The former, based on interval observers, aims to isolate faults during the transient-state operation induced by faults. If the former does not succeed, the latter, based on both interval observers and invariant sets, is started to guarantee FI after the system enters into steady state. Besides, a collection of invariant set-based FDI conditions are established by using all available system-operating information provided by all interval observers. In order to reduce computational complexity, a method to remove all available but redundant/unnecessary system-operating information is incorporated into this approach. If the considered faults satisfy the proposed FDI conditions, it can be guaranteed that they are detectable and isolable after their occurrences. This paper concludes with a case study based on a subsystem of a wind turbine benchmark, which can illustrate the effectiveness of this FDI technique.

In this paper, a fault detection and isolation (FDI) approach using a bank of interval observers is developed. From the methodological point of view, a bank of interval observers is designed according to different dynamical models of the system under different modes (healthy or faulty). Each interval observer matches one system mode while all the interval observers monitor the system simultaneously. In order to guarantee FDI, a set of FDI conditions based on invariant set notions are established. These conditions ensure that the considered faults can be accurately isolated after a period of monitoring time. Finally, simulation results are used to present the effectiveness of the approach.

In this paper, a model-based fault diagnosis methodology for PEM fuel cell systems is presented. The methodology is based on computing residuals using a LPV observer. Fault detection is based on using adaptive threshold generated using an interval observer. Fault isolation is performed using the Euclidean distance between observed relative residuals and theoretical relative sensitivities. To illustrate the results, the commercial fuel cell Ballard Nexa c is used in simulation where a set of typical fault scenarios have been considered. Finally, the diagnosis results corresponding to those fault scenarios are presented. It is remarkable that with this methodology it is possible to diagnose and isolate all the considered faults in contrast with other well known methodologies which use the classic binary signature matrix approach.

We present a robust fault diagnosis method for uncertain multiple input-multiple output (MIMO) linear parameter varying (LPV) parity equations. The fault detection methodology is based on checking whether measurements are inside the prediction bounds provided by the uncertain MIMO LPV parity equations. The proposed approach takes into account existing couplings between the different measured outputs. Modelling and prediction uncertainty bounds are computed using zonotopes. Also proposed is an identification algorithm that estimates model parameters and their uncertainty such that all measured data free of faults will be inside the predicted bounds. The fault isolation and estimation algorithm is based on the use of residual fault sensitivity. Finally, two case studies (one based on a water distribution network and the other on a four-tank system) illustrate the effectiveness of the proposed approach.