Effect of model uncertainty on failure detection: the threshold selector

This paper determines the theoretical limits of failure detectability in systems with modeling errors. Such errors are the main obstacle to accurate failure detection in aircraft, jet engines, power generation systems, and most other systems that include mechanical components and/or thermofluid processes. The paper then introduces the reachable measurement intervals (RMI) failure detection method that performs at the limit of detectability. RMI is capable of detecting the smallest theoretically detectable failures in systems with modeling errors, and is implementable in real time on microprocessors. The method is illustrated on a jet engine example. Nomenclature a — entry of the matrix A A = system matrix b = entry of the matrix B B = input matrix Bj = input matrix equal to the identity matrix C = output matrix C{ = output matrix for additional measurements D = direct transmission matrix H = Pontryagin's function K — matrix of gains for additional measurements L = vector of Lagrange multipliers m = entry of the matrix M M = matrix of Lagrange multipliers associated with uncertain entries of A n = entry of the matrix N N = matrix of Lagrange multipliers associated with uncertain entries of B p = discrete-time index corresponding to current time P = performance index for optimization q - vector of system parameters / = time t0 — current time ts = length of the processing window T - sampling time u = input vector x = state vector y = output vector z - vector of additional measurements Subscripts d = discrete e = uncertain and uncorrelated n = nominal v = uncertain

Nonlinear matrix inequality approach based heading control for an autonomous underwater vehicle with experimental realization

This paper presents a new approach to the design of robust fault detection systems via a genetic algorithm. To achieve robustness, a number of performance indices are introduced, which are expressed in the frequency domain to account for the frequency distributions of incipient faults, noise and modelling uncertainty. All objectives are then reformulated into a set of inequality constraints on performance indices. A genetic algorithm is thus used to search an optimal solution to satisfy these inequality constraints. The approach developed is applied to a flight control system example and results show that incipient sensor faults can be detected reliably in the presence of modelling uncertainty.

PARITY VECTOR COMPENSATION USING NON-LINEAR FILTERING

Fire detection and alarm systems are recognized as key features of a building’s fire prevention and protection strategy. This chapter presents a systematic technique to be used by fire protection engineers in the design and analysis of detection and alarm systems. The majority of discussion is directed toward systems used in buildings. However, many of the techniques and procedures also apply to systems used to protect planes, ships, outside storage yards, and other nonbuilding environments.

This paper presents a novel control method that combines sliding mode controllers, disturbance observers and an exact robust differentiator to solve the underactuated microsatellite electromagnetic docking control problem, in which each docking satellite is equipped with only one single electromagnetic coil. The far-field model of electromagnetic force/torque is given and its model error is analyzed; and a six-degree-of-freedom (6-DOF) docking dynamics model using the far-field model is established. The generation method of the desired trajectory and desired attitude in the docking process is presented; and then an improved exact robust differentiator based on the 3rd-order sliding mode is proposed to produce the desired angular velocity and angular acceleration. In the presence of model uncertainties, external disturbances and input constraints, a disturbance observer-based sliding mode control scheme for underactuated microsatellite electromagnetic docking is developed. The whole closed-loop control system contains an inner-loop attitude controller and an outer-loop position controller. Numerical simulation results are presented to validate the effectiveness and the superior properties of the proposed control scheme.

When the dynamics of an aircraft suddenly or slowly change due to equipment failure or external damage, it is essential that a parameter identification system recognizes this failure quickly in order to predict the control consequences and to take timely appropriate action. There are two groups of parameter identification methods. One group uses the window approach (e.g. Sliding Window and Forgetting Algorithm) which discards old data and therefore automatically adjust to the changed parameters. The other group consists of methods that do not discard old data (e.g. Recursive Least Squares, modified Kalman). These methods need a reset after the failure is detected. Several studies indicated that present failure detection methods have problems with turbulence and the low excitation level typical to cruise flight. This study presents a new failure detection algorithm that enables detection with a minimum number of false alarms and a good performance during turbulence and with noise.

EDUNDANCY management for the purpose of enhaning reliability is performed in three stages: failure detection in which the presence of a failure is discovered, failure isolation in which the failed instrument is identified, and failure correction in which a suitable reorganization of the system is accomplished. Many different failure detection, isolation, and correction (FDIC) algorithms for inertial navigators have appeared in the literature. Eight FDIC algorithms for the dodecahedron configuration *'2 of six strapdown gyros and 6 strapdown accelerometers have been compared in a previous study.3 We shall limit our discussion to the strapdown dodecahedron inertial navigation case although the methods to be developed are applicable to other cases in which the outputs of redundant devices are combined by least squares. All 8 of the FDIC algorithms previously mentioned require a finite period of time to isolate a failure. If the failure causes a large enough error in the instrument output, it can be isolated in less than a minor cycle time so that no erroneous output is used and will be referred to as a hard failure. A failure that causes a smaller error in the instrument output cannot be isolated in less than a minor cycle time so that erroneous output is used. It will be referred to as a soft failure. Thus soft failures degrade system performance while hard failures do not. An algorithm is desired that retroactively removes the effects of soft failures from the navigational solution at the time that the failure is finally isolated. Ideally, only the errors contributed by the instrument since the time of failure would be removed. However the time of failure is unknown, so all of the errors contributed by the failed instrument since initialization of the navigate mode will be removed. Since the FDIC cannot distinguish hard from soft failures, all of the error of the failed instrument is removed even if, in the case of hard failures, no error was introduced by the failure itself. Through the action of the least squares estimator, elimination of the error of one instrument magnifies the effects of the errors of the remaining unfailed instruments to the level they would have reached if the failed instrument had never been present. However this increase in error will usually be less

The traditional mechanical transmission system motion accuracy allocation process has the following problems: the error modeling process can not reflect the error formation mechanism of the system, and the influence of maintenance costs and the motion accuracy robustness of the system are ignored in the process of establishing the optimal allocation model of motion accuracy. In this paper, firstly, meta-action theory is introduced and the meta-action unit is taken as the basic analysis unit, the error modeling of mechanical transmission systems is studied, and the formation mechanism of the motion error is correctly analyzed. Secondly, the comprehensive cost of a mechanical transmission system, considering the part manufacturing cost, assembly cost and maintenance cost per unit, is accurately evaluated using the multi-criteria decision-making (MCDM) method. Thirdly, based on the motion error model, a robust model for system motion accuracy is obtained by analyzing the sensitivity of each motion pair. Then, a multi-objective optimal allocation model of motion accuracy is established. The model is solved by an intelligent algorithm to obtain the Pareto non-dominated solution set, and the optimal solution is selected by the fuzzy set method. Finally, the method described in this paper is illustrated by an engineering example.

The statistical analysis of failure detection decisions in terms of the instantaneous probabilities of false alarm and correct detection for a specified failure magnitude at each check-time have previously been performed for several different failure detection techniques that utilize a Kalman filter. By performing a discrete-time specialization of a result of Gallager and Helstrom on a tightened upper bound for continuous-time level-crossing probabilities, upper bounds on the probabilities of false alarm and correct detection over a time interval have been obtained for the specific technique of CR2 tailnre detection (to allow an accounting for the effect of time correlations of the filter estimates). When these upper bounds are optimized to be as tight as possible to the desired probabilities, the resulting optimization problem for discrete-time is a collection of quadratic programming (QP) problems, which may easily be solved exactly without recourse to approximate solutions as were resorted to in the continuous-time formulation. This technique for evaluating tightened upper bounds on the false alarm and correct detection probabilities may be of general interest, since it can be applied to any failure detection technique or signal detection technique that can relate an exceeding of the deterministic decision threshold by the test statistic directly to a deterministic level being exceeded by a scalar Gaussian random process.

Introduction: Patients diagnosed with heart failure in primary care have a better prognosis than those diagnosed in hospital. However, most cases are missed in the community. Recent attention has focussed on the potential of early detection through screening. Natriuretic peptides (NPs) are tested by GPs and used to rule out heart failure in patients presenting with symptoms. Evidence is now emerging that they may also have a role in screening but their accuracy in this context and the associated optimal thresholds, have not been established. The impact that NP screening would have on patients and health care systems also remains unclear. Methods: We aim to undertake a systematic search of the following sources: Ovid Medline, Embase, Cochrane Database of Systematic Reviews and Cochrane Central Register of Controlled Trials. Screening, data extraction and critical appraisal will be carried out independently and in duplicate by two reviewers. We will include studies based in the community with >100 participants that recruited a screened population. We will not add a study design filter and there will be no language restriction. The primary outcome will be the sensitivity and the specificity of NP screening and optimal thresholds for screening will be explored. Outcomes of interest for the impact analysis will include mortality, hospital admissions and cost effectiveness. This protocol has been developed in accordance with guidelines from the preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P). Discussion: This systematic review will identify how accurately NP screen for heart failure in the community and explore where NP screening thresholds should be set. It also aims to summarise the clinical impact of this strategy. Together, these results should inform future interventions that may provide an alternative pathway to facilitate improved detection of heart failure in the community. Registration: PROSPERO CRD42018087498; registered on 11 May 2018.

Utilising noise to improve an interictal spike detector

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This letter focuses on estimating the local fringe frequency of the interferometric phase, under the hypothesis of superficial scattering. Starting from the formulation of the maximum-likelihood estimator, a new simplified estimator is derived. Due to computational efficiency and robustness versus model errors, the resulting estimator is suited for large data processing in the presence of model uncertainty. Furthermore, such an estimator can be straightforwardly extended to the multibaseline case, resulting in the possibility to estimate the terrain slope with great accuracy. An application to real data is presented, based on a multibaseline ENVISAT data set. </para>

This thesis describes the design, development, and flight-simulation testing of an integrated Artificial Immune System (AIS) for detection, identification, and evaluation of a wide variety of sensor, actuator, propulsion, and structural failures/damages including the prediction of the achievable states and other limitations on performance and handling qualities. The AIS scheme achieves high detection rate and low number of false alarms for all the failure categories considered. Data collected using a motion-based flight simulator are used to define the self for an extended sub-region of the flight envelope. The NASA IFCS F-15 research aircraft model is used and represents a supersonic fighter which include model following adaptive control laws based on non-linear dynamic inversion and artificial neural network augmentation. The flight simulation tests are designed to analyze and demonstrate the performance of the immunity-based aircraft failure detection, identification and evaluation (FDIE) scheme. A general robustness analysis is also presented by determining the achievable limits for a desired performance in the presence of atmospheric perturbations.;For the purpose of this work, the integrated AIS scheme is implemented based on three main components. The first component performs the detection when one of the considered failures is present in the system. The second component consists in the identification of the failure category and the classification according to the failed element. During the third phase a general evaluation of the failure is performed with the estimation of the magnitude/severity of the failure and the prediction of its effect on reducing the flight envelope of the aircraft system.;Solutions and alternatives to specific design issues of the AIS scheme, such as data clustering and empty space optimization, data fusion and duplication removal, definition of features, dimensionality reduction, and selection of cluster/detector shape are also analyzed in this thesis. They showed to have an important effect on detection performance and are a critical aspect when designing the configuration of the AIS.;The results presented in this thesis show that the AIS paradigm addresses directly the complexity and multi-dimensionality associated with a damaged aircraft dynamic response and provides the tools necessary for a comprehensive/integrated solution to the FDIE problem. Excellent detection, identification, and evaluation performance has been recorded for all types of failures considered. The implementation of the proposed AIS-based scheme can potentially have a significant impact on the safety of aircraft operation. The output information obtained from the scheme will be useful to increase pilot situational awareness and determine automated

This paper describes Team Tidyboy's approach to build a functional, robust, yet highly modular software framework for home service robots. Home service tasks require a number of software modules to work together, which includes verbal and nonverbal human robot interactions, 3D perception of known and unknown objects and manipulation of various objects using movement of both the manipulator and the base of the robot. World Robot Summit (WRS) 2020 Partner Robot Challenge (PRC) rule has raised the bar higher by asking for a reliable, long-term operation of such a system without human intervention. We present how we have overcome such challenges by robust software modules, repeated experiments in randomly initialized simulated environments, and finally introduction of various failure detection and recovery algorithms. Our software is successfully validated in RoboCup@Home 2021 and WRS 2020 PRC remote participation league, which led us to obtaining the first places in both competitions.