Optimally Robust Redundancy Relations for Failure Detection in Uncertain 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

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.

Guaranteed Active Failure Detection and Isolation for Linear Dynamical Systems

ROBUST FAILURE DETECTION AND ISOLATION IN NON-LINEAR CONTROL SYSTEMS

Recent advances in microelectronic circuit have enabled the application of process diagnostics to a variety of systems to improve performances and the reliability. Failure detection and isolation strategies monitor a system for degradations and if detected, classify the failure source. One of the most important methods for failure detection and diagnosis is the analysis of the variations of the estimated parameter of DC Motor. In this paper is presented a structure of a method for failure detection and diagnosis based on continuous time parameter estimation of a mathematical model of the DC Motor. Continuous time parameters can be related easily to the physical characterizations of DC Motor. The parameter is directly estimated by well-known prediction error method of identification for dynamic systems. The failure detection it is based on changing the process coefficient and statistical decision methods. Unlike usual papers, the failure can be detected whether others process parameters are not constant.

Among the fastest‐growing applications of high‐precision GPS positioning are those which are kinematic in nature. Carrier phase‐based GPS positioning of a moving antenna—for example, attached to a ship, an aircraft, or a land vehicle—is now commonplace. Recent software innovations make use of advanced ambiguity resolution “on the fly” and real‐time kinematic data processing algorithms to emulate the ease of operation of conventional differential GPS (DGPS) based on transmitted pseudo‐range corrections. However, as much higher accuracy must now be assured compared to DGPS, greater attention must be focused on the quality control aspects of GPS positioning. This study describes two methods for detecting failures or changes of small magnitude in real time in GPS measurements. Examination of the overlap or disjointedness of robust and conventional confidence intervals and studentized normal variates have been used as failure detection tools. These methods are based on testing the performance of the differences between the conventional (nonrobust) Kalman state estimates and the robust Kalman filler estimates. Detection of cycle slips in carrier phase data, outliers in phase rate or in code ranges, or any other type of disorder in the measurements of the GPS system can be addressed with these failure detection methods. Application and evaluation of the algorithms has been carried out using raw carrier‐phase and phase‐rate GPS measurements. It has been demonstrated that these failure detection tools provide powerful and efficient diagnostics for detecting small changes in the measurements of the GPS system.

Failure detection methods are of significant interest for photovoltaic (PV) site operators to help reduce gaps between expected and observed energy generation. Current approaches for field-based fault detection, however, rely on multiple data inputs and can suffer from interpretability issues. In contrast, this work offers an unsupervised statistical approach that leverages hidden Markov models (HMM) to identify failures occurring at PV sites. Using performance index data from 104 sites across the United States, individual PV-HMM models are trained and evaluated for failure detection and transition probabilities. This analysis indicates that the trained PV-HMM models have the highest probability of remaining in their current state (87.1% to 93.5%), whereas the transition probability from normal to failure (6.5%) is lower than the transition from failure to normal (12.9%) states. A comparison of these patterns using both threshold levels and operations and maintenance (O&M) tickets indicate high precision rates of PV-HMMs (median = 82.4%) across all of the sites. Although additional work is needed to assess sensitivities, the PV-HMM methodology demonstrates significant potential for real-time failure detection as well as extensions into predictive maintenance capabilities for PV.

Robust Failure Detection and Isolation in Non-Linear Control 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.

ABSTRACTThis paper presents a review and analysis of current non-destructive failure detection methods of composite materials and a brief outline of the build of a bamboo bicycle which has been used as a development platform and test bed for the initial development of a novel and practical non-destructive failure detection solution, which has future compatibility for carbon-fibre (CF)-based bicycles. The paper begins by presenting the current market condition of composite materials and in particular, CF and CF-reinforced plastic, and then follows onto failure modes and proceeds to investigate a comprehensive range of failure detection methods.

A medical asset tracking system monitors the in/out status of a medical asset with a mobile node which moves around a monitoring area, leaves it, and then returns to the region repeatedly. Due to failure of the mobile nodes, the system may determine that a mobile asset is outside the region despite the fact that the mobile asset is inside. In this paper, a novel failure detection method is proposed to resolve this problem. It uses two properties of the neighboring nodes of a mobile node disconnected from a sensor network. One is the trend of the neighbor counts; the other is that of the ratios of the boundary nodes contained in the neighbors. With these trends, the proposed method detects failed nodes from mobile nodes disconnected from a sensor network. The experimental results show that the proposed method can detect most failures of mobile nodes, including those that the conventional approaches fail to observe.

Traditional positioning methods, such as conventional Real Time Kinematic (cRTK) rely upon local reference networks to enable users to achieve high-accuracy positioning. The need for such relatively dense networks has significant cost implications. Precise Point Positioning (PPP) on the other hand is a positioning method capable of centimeter-level positioning without the need for such local networks, hence providing significant cost benefits especially in remote areas. This paper presents the state-of-the-art PPP method using both GPS and GLONASS measurements to estimate the float position solution before attempting to resolve GPS integer ambiguities. Integrity monitoring is carried out using the Imperial College Carrier-phase Receiver Autonomous Integrity Monitoring method. A new method to detect and exclude GPS base-satellite failures is developed. A base-satellite is a satellite whose measurements are differenced from other satellite’s measurements when using between-satellite-differenced measurements to estimate position. The failure detection and exclusion methods are tested using static GNSS data recorded by International GNSS Service stations both in static and dynamic processing modes. The results show that failure detection can be achieved in all cases tested and failure exclusion can be achieved for static cases. In the kinematic processing cases, failure exclusion is more difficult because the higher noise in the measurement residuals increases the difficulty to distinguish between failures associated with the base-satellite and other satellites.

A failure detection and isolation (FDI) method for validation of ultrasonic ranging sensor (URS) signals in robot position control systems is presented. The technique builds on the concepts of parity space and analytic redundancy, where integration of analytic and sensor redundancy provides a direct, reliable method for measuring the end-effector position of a robot relative to the world coordinates. These measurements are not influenced by deflections caused by the payload, accumulated joint measurement errors in a serial mechanism, or computational errors in executing kinematic relationships. The position control system's insensitivity to structural deflections allows the robot to handle larger payloads. Simulation results are presented to demonstrate how the FDI technique can be applied. >

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.