Guaranteed Active Failure Detection and Isolation for Linear Dynamical Systems

Active and Passive Methods of Detecting Parkinson's Disease.

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.

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

Failure Detection of Linear Dynamical Systems by a Generalized-likelihood-ratio Method Based on a Detectability Analysis

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.

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. >

Software-Defined Networking (SDN) is a networking approach that separates the network's control plane and data plane to provide better network control. The nature of SDN requires an essential focus on its reliability and availability, which includes failure detection aspects. Several techniques have been proposed to enhance SDN failure detection, including passive and active approaches. However, the Route Inspection (RI) algorithm, an example of the active probing method in active detection, has scalability issues when deployed in large-scale networks as it generates inefficient and lengthy probe paths. To address this, we propose using community detection (CD) algorithms to improve the probe path generation of the RI algorithm, resulting in the 'Failure Detection using Community Detection and probing (FDCD)' framework. With the aim to reduce travel paths, the proposed framework minimizes the network topology using CD algorithms to reduce the topology size into smaller communities. Then, the RI algorithm calculates the probe path in the communities instead of the single large topology, thus improving the efficiency of the failure detection mechanism. This paper adopts three community detection algorithms: Louvain, Label Propagation, and Girvan-Newman. We evaluate the framework using three well-known network topologies, COST266, PIORO40, and GERMANY50, in Mininet to observe the framework's performance. The results indicate that the proposed approach improves the algorithm's scalability against its baseline approach, improving the average probe round trip time by 66.87% and the average path installation time by 2.67%.

In this study, clustering based method for activation detection in functional magnetic resonance imaging (fMRI) is employed. Moreover, some features are obtained by fitting two models namely FIR filter and Gamma function, to hemodynamic response function (HRF). After applying clustering methods (that require number of clusters as an input) to feature space, our simulations show that number of clusters can affect activation detection significantly. Therefore a newly proposed clustering algorithm namely evolving neural gas (ENG) that gives optimal number of clusters is exploited. In addition to ENG, the result of four clustering algorithms namely k-means, fuzzy C-means, neural gas, and clara in different number of clusters are evaluated. The results show that the best activation detection is taken place using obtained optimal number of clusters.

Failure detection is one of the basic functions of building a reliable disaster recovery backup system. Aiming at the application-level disaster recovery backup failure detection problem, this paper analyzes the remote disaster recovery center architecture and failure detection hierarchy, and predicts the arrival time of cross-domain heartbeat information through the back propagation neural network based on particle swarm optimization (PSO-BP). When the actual timeout is reached, the active Auxiliary Detection (AD) is used to improve the correctness of failure detection, and finally the effectiveness of method PSO-BP-AD is verified through simulation.

Seven algorithms for failure detection, isolation, and correction of strapdown inertial instruments in the dodecahedron configuration are competitively evaluated by means of a digital computer simulation that provides them with identical inputs. Their performance is compared in terms of orientation errors and computer burden. The analytical foundations of the algorithms are presented. The features that are found to contribute to superior performance are use of a definite logical structure, elimination of interaction between failures, different thresholds for first and second failures, use of the 'parity' test signals, and avoidance of iteration loops.

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.

The knowledge of the protein functions is very important in the development of new drugs. Many experimental methods for determining protein function exist, but due to their complexity the number of protein structures with unknown functions is rapidly growing. So, there is an obvious necessity for development of computer methods for annotating protein structures. In this paper we present a fuzzy decision tree based method for protein active sites detection, which could be used for annotating protein structures. We extract several features of the amino acids, and then using different membership functions we build fuzzy decision trees in order to detect the possible active sites. We provide some experimental results of the evaluation of our method. Additionally, our method is compared with several existing methods for protein active sites detection.