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Failure Detection (failure + detection)
Selected AbstractsModel-Based Failure Detection on Nonlinear Systems: Theory and TransitionNAVAL ENGINEERS JOURNAL, Issue 2 2007KIMBERLY J. DRAKE Failure detection is an active area of Navy research due to its many important applications. Recently, an approach for multi-model identification and fault detection in the presence of bounded energy noise over finite time intervals has been introduced. This family of algorithms was originally designed to work on linear systems that can be modeled analytically. In this paper, efforts made toward extending this algorithm for fault detection to nonlinear systems along with efforts in testing this family of algorithms on real systems are discussed. [source] Detecting Cycle Failures at Signalized Intersections Using Video Image ProcessingCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 6 2006Jianyang Zheng Cycle failure detection is essential for identifying signal control problems at intersections. However, typical traffic sensors do not have the capability of capturing cycle failures. In this article, we introduce an algorithm for traffic signal cycle failure detection using video image processing. A cycle failure for a particular movement occurs when at least one vehicle must wait through more than one red light to complete the intended movement. The proposed cycle failure algorithm was implemented using Microsoft Visual C#. The system was tested with field data at different locations and time periods. The test results show that the algorithm works favorably: the system captured all the cycle failures and generated only three false alarms, which is approximately 0.9% of the total cycles tested. [source] UniFAFF: a unified framework for implementing autonomic fault management and failure detection for self-managing networksINTERNATIONAL JOURNAL OF NETWORK MANAGEMENT, Issue 4 2009Ranganai Chaparadza Today's network management, as known within the Fault, Configuration, Accounting, Performance, Security (FCAPS) management framework, is moving towards the definition and implementation of ,self-managing' network functions, with the aim of eliminating or drastically reducing human intervention in some of the complex aspects or daunting tasks of network management. The fault management plane of the FCAPS framework deals with the following functions: fault detection, fault diagnosis, localization or isolation, and fault removal. Task automation is at the very heart of self-managing (autonomic) nodes and networks, meaning that all functions and processes related to fault management must be automated as much as possible within the functionalities of self-managing (autonomic) nodes and networks, in order for us to talk about autonomic fault management. At this point in time there are projects calling for implementing new network architectures that are flexible to support on-demand functional composition for context- or situation-aware networking. A number of such projects have started, under the umbrella of the so-called clean-slate network designs. Therefore, this calls for open frameworks for implementing self-managing (autonomic) functions across each of the traditional FCAPS management planes. This paper presents a unified framework for implementing autonomic fault management and failure detection for self-managing networks, a framework we are calling UniFAFF. Copyright © 2008 John Wiley & Sons, Ltd. [source] Analyzing unidentified locked-joint failures in kinematically redundant manipulatorsJOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 1 2005Manish Goel Robots are frequently used for operations in hostile environments. The very nature of these environments, however, increases the likelihood of robot failures. Common failure-tolerance techniques rely on effective failure detection and identification. Since a failure may not always be successfully identified, or, even if identified, may not be identified soon enough, it becomes important to consider the behavior of manipulators with unidentified failures. This work investigates the behavior of robots experiencing unidentified locked-joint failures in a general class of tasks characterized by point-to-point motion. Based on the analysis, a procedure for workspace evaluation is developed that allows for the identification of regions in the manipulator's workspace in which tasks may be completed even with such failures. © 2005 Wiley Periodicals, Inc. [source] Robust disturbance attenuation for discrete-time active fault tolerant control systems with uncertaintiesOPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 2 2003Peng Shi Abstract The problems of stochastic stability and stochastic disturbance attenuation for a class of linear discrete-time systems are considered in this paper. The system under study is a state space model possessing two Markovian jump parameters: one is failure process and another is failure detection and isolation scheme. A controller is designed to guarantee the stochastic stability and a disturbance attenuation level. Robustness problems for the above system with norm-bounded parameter uncertainties are also investigated. It is shown that the uncertain system can be robustly stochastically stabilized and have a robust disturbance attenuation level for all admissible perturbations if a set of coupled Riccati inequalities has solutions. A numerical example is given to show the potential of the proposed technique. Copyright © 2003 John Wiley & Sons, Ltd. [source] Application of a capacitive transducer for online part weight prediction and fault detection in injection moldingPOLYMER ENGINEERING & SCIENCE, Issue 4 2007Ka Tsai Fung This article extends the applications of in-mold capacitive transducer to packing stage for on-line part weight prediction and check-ring failure detection for injection molding. Experiments under various conditions show that the proposed on-line part weight prediction is robust and effective, which may pave the way for on-line closed-loop part weight control. POLYM. ENG. SCI., 47:347,353, 2007. © 2007 Society of Plastics Engineers. [source] | ||||||||||