A Failure Detection System for Large Scale Distributed Systems

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

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.

Failure Detection in Linear Discrete Dynamical Systems and its Detectionability

SIMULATION STUDY OF AN ANALYTICALLY-REDUNDANT FLIGHT CONTROL SYSTEM

A link failure in the path of a virtual circuit in a packet data network will lead to premature disconnection of the circuit by the end‐points. A soft failure will result in degraded throughout over the virtual circuit. If these failures can be detected quickly and reliably, then appropriate rerouteing strategies can automatically reroute the virtual circuits that use the failed facility.In this paper, we develop a methodology for analysing and designing failure detection schemes for digital facilites. Based on errored second data, we develop a Markov model for the error and failure behaviour of a T1T1 carrier, the lowest level in the plesiochronous digital carrier hierarchy in the United States. AT1 carrier has a payload of 24 64Kbps PCM channels. trunk. The performance of a detection scheme is characterized by its false alarm probability and the detection delay. Using the Markov model, we analyse the performance of detection schemes that use physical layer or link layer information. The schemes basically rely upon detecting the occurrence of severely errored seconds (SESs). A failure is declared when a counter, that is driven by the occurrence of SESs, reaches a certain threshold.For hard failures, the design problem reduces to a proper choice of the threshold at which failure is declared, and on the connection. reattempt parameters of the virtual circuit end‐point session recovery procedures. For soft failures, the performance of a detection scheme depends, in addition, on how long and how frequent the error bursts are in a given failure mode.We also propose and analyse a novel Level 2 detection scheme that relies only upon anomalies observable at Level 2, i.e. CRC failures and idle‐fill flag errors. Our results suggest that Level 2 schemes that perform as well as Level 1 schemes are possible.

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.

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.

In an active approach for model detection and its use in failure detection, an auxiliary control is applied in order to assist in model identification. An active approach for robust multi-model identification and failure detection in the presence of disturbances over possibly short time intervals has been introduced. Previous papers extended the original design procedure to problems with several delays and additive noise. The infinite dimensional delay problem was approximated by a finite dimensional system without delays and numerical examples were worked. The numerical examples seemed to converge better than was expected. In this paper we explain that convergence. In addition we discuss the extension of the previous results for delay systems with additive noise to those with small model uncertainties and explain why this additional assumption of small uncertainty, which was not present in the case without delays, is now needed for the analysis.

Automation systems become more and more complex, and their traditional hierarchical and centralized control may not be able to deal with this ever-increasing complexity satisfactorily. Decentralized approaches are considered promising, but they are not yet sufficiently understood for widespread industrial application. In particular, agent-based control has not yet achieved its potential, so that more research is still required. Our approach is based on the concept of an automation agent composed of a hardware (HW) component and a software (SW) component, where the HW component can be viewed as an embodiment of this SW component. These agents represent the parts of a flexible automation system. In particular, we propose that each such agent has an explicit symbolic representation of itself and of its relations to its environment. More precisely, this new agent architecture contains a reflective world model that includes a representation of this same agent and of its relations to its environment. In addition, the world model contains representations of situations and activities. We show that and how such a self-representation can be used for self-configuration, as well as monitoring, including failure detection in automation systems. The agent performs this self-configuration without any human intervention and just based on a brief specification of the HW configuration. The monitoring is based on automatically raised expectations for the system's behavior and allows failure detection without any specific failure detection code.

Guaranteed Active Failure Detection and Isolation for Linear Dynamical Systems

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.

Electric Light bulb failures are common in residential, commercial buildings, and on the city streets. For light bulb failure detection, manual or complex building automation solutions are adopted. In important buildings, the maintenance personals inspect the lighting fixtures periodically. Building automation solutions cannot detect individual bulb failures. The manual inspection approach is exhaustive and may not give complete failure information. This paper suggests an IoT-based simple solution which, will detect individual electric light bulb failures and automatically send email notification alerts along with audio messages about the electric light bulb failures to the concerned maintenance staff. The proposed method is nondestructive and can be adopted in any building or on the streets. A prototype design of the proposed system has been presented. The presented scheme is a humble solution for detecting light bulb failures inside and outside the two holy mosques in Makkah and Madinah, Saudi Arabia. The proposed design may be applied to any light on streets or inside pedestrian tunnels or pathways to detect light failures promptly to avoid any causality or stampede.

As the automotive industry becomes increasingly competitive, parts manufacturers are under extreme pressure to improve the quality of their parts, while at the same time reducing costs. The method currently used to detect failures in automotive light assemblies during vibration endurance testing involves manual inspection only after the test is completed. In most cases, this method does not allow the first onset of failure to be determined. Subsequent damage caused by the ongoing test after the initial failure often obliterates the clues to what failed first, thus diminishing the usefulness of the information collected as input to design improvement iterations. An adaptable, reliable, and low-cost real-time monitoring and diagnostic system that would interrupt the testing operation at the first onset of a failure is desired. This paper describes an accelerometer-based and a microphone-based monitoring system for automotive light assembly failure detection during endurance testing. The vibration and acoustic signal analysis methods are also described. Preliminary results from these two systems show a significant difference between healthy and faulty fog light assemblies, which can be detected. This suggests that there is potential to develop these and potentially other measures for use in an adaptable, reliable, and low-cost real-time monitoring and diagnostic system that would interrupt the testing operation at the first onset of a failure. Further testing is planned to determine the sensitivity of the methods used in this study to detect the earliest onset of failure damage during endurance testing of automotive light assemblies.

STATIC SHAPE ESTIMATION AND FAILURE DETECTION IN LARGE SPACEBORNE ANTENNAS VIA COMPUTER VISION

Measurement of energy to cover heat losses due to hot water recirculation must be precise so that the associated costs can be split among the consumers in a fair way, but it is very challenging due to the low ratio of consumed water to recirculated water. This paper reviews two approved methods of measurement and discusses their advantages and disadvantages. Additionally, it introduces a software enhancement of measurement system that can be used to detect failures in the system, such as leaks. Test of the system and its ability to indicate failures is explored using real data.