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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
Effects of indicating failure odds and smoothed outputs on human failure detection in dynamic systems∗
Effects of indicating failure odds and smoothed outputs on human failure detection in dynamic systems∗
Effects of indicating failure odds and smoothed outputs on human failure detection in dynamic systems : Tzelgov, J., Tsach, U., and Sheridan, T.B.Ergonomics, 1985, 28.2, 449–462
Effects of indicating failure odds and smoothed outputs on human failure detection in dynamic systems : Tzelgov, J., Tsach, U., and Sheridan, T.B.Ergonomics, 1985, 28.2, 449–462
Read moreModified Schemes of Sequential Probability Ratio Test for Incipient Failure Detection in Dynamic Systems
Modified Schemes of Sequential Probability Ratio Test for Incipient Failure Detection in Dynamic Systems
21Optimally Robust Redundancy Relations for Failure Detection in Uncertain Systems
All failure detection methods are based, either explicitly or implicitly, on the use of redundancy, i.e. on (possibly dynamic) relations among the measured variables.The robustness of the failure detection process consequently depends to a great degree on the reliability of the redundancy relations, which in turn is affected by the inevitable presence of model uncertainties.In this paper we address the problem of determining redundancy relations that are optimally robust, in a sense that includes several major issues .ofimportance in practical failure detection, and that provides a significant amount of intuition concerning the geometry of robust failure detection.We also give a procedure, involving the construction of a single matrix and its singular value decomposition, for the determination of a complete sequence of redundancy relations, ordered in terms of their level of robustness.This procedure also provides the basis for comparing levels of robustness in redundancy provided by different sets of sensors.
Read more11Failure Detection and Location in Process Control - Integrating a New Model based Technique with Other Methods
The failure detection and location method (FDLM) is discussed. The key idea is to utilize the causal description of the system's power flows. This description is used to detect and locate system failures without modeling the possible system failure modes. The FDLM specifies those system and model outputs the comparison of which maximize the possibilities of detecting existing system failures. An example of an FDLM application in a simulated fossil fuel power plant demonstrates that the method is applicable to systems which are modeled by bilateral as well as unilateral and multilateral energy bonds. An integration of the FDLM with other available failure detection methods seems to result in an effective and promising failure detection scheme.
Read more113Guaranteed Active Failure Detection and Isolation for Linear Dynamical Systems
Guaranteed Active Failure Detection and Isolation for Linear Dynamical Systems
10Robust Detection and Estimation in Dynamic Systems and Statistical Signal Processing: Intersections, Parallel Paths and Applications
Robust Detection and Estimation in Dynamic Systems and Statistical Signal Processing: Intersections, Parallel Paths and Applications
Read more4Failure Detection in Linear Discrete Dynamical Systems and its Detectionability
Failure Detection in Linear Discrete Dynamical Systems and its Detectionability
12A Failure Detection System for Large Scale Distributed Systems
Failure detection is a fundamental building block for ensuring fault tolerance in large scale distributed systems. It is also a difficult problem. Resources under heavy loads can be mistaken as being failed. The failure of a network link can be detected by the lack of a response, but this also occurs when a computational resource fails. Although progress has been made, no existing approach provides a system that covers all essential aspects related to a distributed environment. This paper presents a failure detection system based on adaptive, decentralized failure detectors. The system is developed as an independent substrate, working asynchronously and independent of the application flow. It uses a hierarchical protocol, creating a clustering mechanism that ensures a dynamic configuration and traffic optimization. It also uses a gossip strategy for failure detection at local levels to minimize detection time and remove wrong suspicions. Results show that the system scales with the number of monitored resources, while still considering the QoS requirements of both applications and resources.
Read more388Effect of model uncertainty on failure detection: the threshold selector
The performance of all failure detection, isolation, and accommodation (DIA) algorithms is influenced by the presence of model uncertainty. The authors present a unique framework to incorporate a knowledge of modeling error in the analysis and design of failure detection systems. A concept is introduced called the threshold selector, which is a nonlinear inequality whose solution defines the set of detectable sensor failure signals. It identifies the optimal threshold to be used in innovations-based DIA algorithms. The optimal threshold is shown to be a function of the bound on modeling errors, the noise properties, the speed of DIA filters and the classes of reference and failure signals. The size of the smallest detectable failure is also determined. The results are applied to a multivariable turbofan jet engine example, which demonstrates improvements compared to previous studies.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Read more2Failure Detection of Linear Dynamical Systems by a Generalized-likelihood-ratio Method Based on a Detectability Analysis
Failure Detection of Linear Dynamical Systems by a Generalized-likelihood-ratio Method Based on a Detectability Analysis
SIMULATION STUDY OF AN ANALYTICALLY-REDUNDANT FLIGHT CONTROL SYSTEM
SIMULATION STUDY OF AN ANALYTICALLY-REDUNDANT FLIGHT CONTROL SYSTEM
3A method for the detection and isolation of system failures
A method is presented which allows the detection and isolation of failures in either the estate of a stochastic linear system or the sensor variable associated with that system. The method is based on an application of multiple hypothesis testing, Kalman filtering and sequential maximum likelihood estimation.
Read more8Multifunctional composite structures with embedded conductive yarns for shock load monitoring and failure detection
This study evaluates the performance of composite structures with embedded conductive yarns during shock loads to create a multifunctional system for immediate failure detection. The scalable sensing yarns were made by braiding Kevlar fibers with Nitinol fibers and then integrating them into a carbon/epoxy prepreg. The multifunctional structure was subjected to a Mach 2 air blast load using a shock tube apparatus. The embedded sensor yarns were used to record their electrical performance, while Digital Image Correlation captured full-field displacements, velocities, and strains. In addition, pressure transducers measured shock event pressures. The results revealed that through-thickness failure of the laminated composite occurred at approximately 2.5% strain, which was visually observable. However, the embedded sensor exhibited out-of-range electrical measurements at around 1.5% strain, even though no visible structural damage was present. This demonstrates the embedded sensing yarns’ ability to detect delamination-type failures by responding to interlaminate damage, highlighting their advantages over conventional external sensors. Similarly, the gauge factor for the fiber system was determined to be 1.89 ± 0.07. This multifunctional system shows great potential for enhancing composite structure safety and performance in high-performance aerospace applications and offering real-time structural health assessment.
Read more33Autonomous failure detection, identification and fault-tolerant estimation with aerospace applications
In this paper, we propose a novel approach for Failure Detection and Identification (FDI) in nonlinear systems based on the Interacting Multiple Model (IMM) Extended Kalman Filter (EKF) approach. In the nonlinear system FDI application, the main idea consists of representing each failure mode by a model and combining the outputs of EKF's based on different models in a near-optimal way. This IMM-FDI filter provides not only failure detection and identification but also a near-optimal estimate of the system state (even during a failure). The approach has been applied successfully to a problem of spacecraft autonomy for the detection and identification of sensor (gyro, star tracker) and actuator failures. The results of this application show that IMM-EKF detects and identifies failures much more rapidly and reliably than the multi-hypothesis EKF. Furthermore, it handles satisfactorily both permanent and transient failures. Current efforts are underway to perform extensive validation testing on high-fidelity simulation models of representative spacecraft.
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