Showing papers on "Fault detection and isolation published in 2004"

Adaptive fault-tolerant control of nonlinear uncertain systems: an information-based diagnostic approach

Abstract: This paper presents a unified methodology for detecting, isolating and accommodating faults in a class of nonlinear dynamic systems. A fault diagnosis component is used for fault detection and isolation. On the basis of the fault information obtained by the fault-diagnosis procedure, a fault-tolerant control component is designed to compensate for the effects of faults. In the presence of a fault, a nominal controller guarantees the boundedness of all the system signals until the fault is detected. Then the controller is reconfigured after fault detection and also after fault isolation, to improve the control performance by using the fault information generated by the diagnosis module. Under certain assumptions, the stability of the closed-loop system is rigorously investigated. It is shown that the system signals remain bounded and the output tracking error converges to a neighborhood of zero.

A taxonomy and catalog of runtime software-fault monitoring tools

Abstract: A goal of runtime software-fault monitoring is to observe software behavior to determine whether it complies with its intended behavior. Monitoring allows one to analyze and recover from detected faults, providing additional defense against catastrophic failure. Although runtime monitoring has been in use for over 30 years, there is renewed interest in its application to fault detection and recovery, largely because of the increasing complexity and ubiquitous nature of software systems. We present taxonomy that developers and researchers can use to analyze and differentiate recent developments in runtime software fault-monitoring approaches. The taxonomy categorizes the various runtime monitoring research by classifying the elements that are considered essential for building a monitoring system, i.e., the specification language used to define properties; the monitoring mechanism that oversees the program's execution; and the event handler that captures and communicates monitoring results. After describing the taxonomy, the paper presents the classification of the software-fault monitoring systems described in the literature.

Fault Diagnosis: Models, Artificial Intelligence, Applications

Abstract: 1. Introduction.- 2. Models in the diagnostics of processes.- 3. Process diagnostics methodology.- 4. Methods of signal analysis.- 5. Control theory methods in designing diagnostic systems.- 6. Optimal detection observers based on eigenstructure assignment.- 7. Robust H?-optimal synthesis of FDI systems.- 8. Evolutionary methods in designing diagnostic systems.- 9. Artificial neural networks in fault diagnosis.- 10. Parametric and neural network Wiener and Hammerstein models in fault detection and isolation.- 11. Application of fuzzy logic to diagnostics.- 12. Observers and genetic programming in the identification and fault diagnosis of non-linear dynamic systems.- 13. Genetic algorithms in the multi-objective optimisation of fault detection observers.- 14. Pattern recognition approach to fault diagnostics.- 15. Expert systems in technical diagnostics.- 16. Selected methods of knowledge engineering in systems diagnosis.- 17. Methods of acqusition of diagnostic knowledge.- 18. State monitoring algorithms for complex dynamic systems.- 19. Diagnostics of industrial processes in decentralised structures.- 20. Detection and isolation of manoeuvres in adaptive tracking filtering based on multiple model switching.- 21. Detecting and locating leaks in transmission pipelines.- 22. Models in the diagnostics of processes.- 23. Diagnostic systems.

An Overview of Intelligent Fault Detection in Systems and Structures

Abstract: This paper describes a coherent strategy for intelligent fault detection. All of the features of the strategy are discussed in detail. These encompass: (i) a taxonomy for the relevant concepts, i.e. a precise definition of what constitutes a fault etc., (ii) a specification for operational evaluation which makes use of a hierarchical damage identification scheme, (iii) an approach to sensor prescription and optimisation and (iv) a data processing methodology based on a data fusion model.

Fault-tolerant voltage-fed PWM inverter AC motor drive systems

Abstract: This paper shows how to integrate fault compensation strategies into two different types of configurations of induction motor drive systems. The proposed strategies provide compensation for open-circuit and short-circuit failures occurring in the converter power devices. The fault compensation is achieved by reconfiguring the power converter topology with the help of isolating and connecting devices. These devices are used to redefine the post-fault converter topology. This allows for continuous free operation of the drive after isolation of the faulty power switches in the converter. Experimental results demonstrate the validity of the proposed systems.

Techniques to Reduce the Soft Error Rate of a High-Performance Microprocessor

Abstract: Transient faults due to neutron and alpha particle strikes posea significant obstacle to increasing processor transistor counts infuture technologies. Although fault rates of individual transistorsmay not rise significantly, incorporating more transistors into adevice makes that device more likely to encounter a fault. Hence,maintaining processor error rates at acceptable levels will requireincreasing design effort.This paper proposes two simple approaches to reduce errorrates and evaluates their application to a microprocessor instructionqueue. The first technique reduces the time instructions sit invulnerable storage structures by selectively squashing instructionswhen long delays are encountered. A fault is less likely to cause anerror if the structure it affects does not contain valid instructions.We introduce a new metric, MITF (Mean Instructions To Failure),to capture the trade-off between performance and reliability introducedby this approach.The second technique addresses false detected errors. In theabsence of a fault detection mechanism, such errors would nothave affected the final outcome of a program. For example, a faultaffecting the result of a dynamically dead instruction would notchange the final program output, but could still be flagged by thehardware as an error. To avoid signalling such false errors, wemodify a pipeline's error detection logic to mark affected instructionsand data as possibly incorrect rather than immediately signalingan error. Then, we signal an error only if we determine laterthat the possibly incorrect value could have affected the program'soutput.

Electrical utility communications and control system

Abstract: A system for managing collection of data and remote operation of fielded remote units is disclosed The remote units may be incorporated in automatic meter reading systems, capacitor bank switching systems, power line fault detection units, power recloser units, surveillance systems, railroad switch heaters, or any of a multitude of systems wherein widely scattered devices require monitoring and/or operational commands Each remote unit is provided with a CELLEMETRY™ transceiver, allowing the unit to receive commands from and pass data to a data center via the cellular control channel network and the Internet The data center is organized to provide data related to a particular service to an associated customer user via the Internet, the customer users being utility companies, railroad companies, surveillance companies, and the like Particularly, electrical, gas and water utilities may advantageously utilize Applicant's system for automatic meter reading, prepaid utilities, fault location in 3 phase power, preventative power outage monitoring, and power outage monitoring

Selecting a Cost-Effective Test Case Prioritization Technique

Abstract: Regression testing is an expensive testing process used to validate modified software and detect whether new faults have been introduced into previously tested code. To reduce the cost of regression testing, software testers may prioritize their test cases so that those which are more important, by some measure, are run earlier in the regression testing process. One goal of prioritization is to increase a test suite's rate of fault detection. Previous empirical studies have shown that several prioritization techniques can significantly improve rate of fault detection, but these studies have also shown that the effectiveness of these techniques varies considerably across various attributes of the program, test suites, and modifications being considered. This variation makes it difficult for a practitioner to choose an appropriate prioritization technique for a given testing scenario. To address this problem, we analyze the fault detection rates that result from applying several different prioritization techniques to several programs and modified versions. The results of our analyses provide insights into which types of prioritization techniques are and are not appropriate under specific testing scenarios, and the conditions under which they are or are not appropriate. Our analysis approach can also be used by other researchers or practitioners to determine the prioritization techniques appropriate to other workloads.

Fault detection and diagnosis

Abstract: A network troubleshooting framework is described. In an implementation, a method includes detecting discrepancy in operation of a network by supplying data that describes the network to a network simulation so that the network simulation provides an estimation of network performance. A determination is made as to whether the estimation of network performance differs from observed network performance of the network. A root cause of the discrepancy is diagnosed by injecting one or more of a plurality of faults into the network simulation until the estimation of network performance approximates the observed network performance.

Negative Selection Algorithm for Aircraft Fault Detection

Abstract: We investigated a real-valued Negative Selection Algorithm (NSA) for fault detection in man-in-the-loop aircraft operation. The detection algorithm uses body-axes angular rate sensory data exhibiting the normal flight behavior patterns, to generate probabilistically a set of fault detectors that can detect any abnormalities (including faults and damages) in the behavior pattern of the aircraft flight. We performed experiments with datasets (collected under normal and various simulated failure conditions) using the NASA Ames man-in-the-loop high-fidelity C-17 flight simulator. The paper provides results of experiments with different datasets representing various failure conditions.

Conflicts versus analytical redundancy relations: a comparative analysis of the model based diagnosis approach from the artificial intelligence and automatic control perspectives

Abstract: Two distinct and parallel research communities have been working along the lines of the model-based diagnosis approach: the fault detection and isolation (FDI) community and the diagnostic (DX) community that have evolved in the fields of automatic control and artificial intelligence, respectively. This paper clarifies and links the concepts and assumptions that underlie the FDI analytical redundancy approach and the DX consistency-based logical approach. A formal framework is proposed in order to compare the two approaches and the theoretical proof of their equivalence together with the necessary and sufficient conditions is provided.

Real-time fault diagnosis [robot fault diagnosis]

Abstract: This article presents a number of complementary algorithms for detecting faults on-board operating robots, where a fault is defined as a deviation from expected behavior. The algorithms focus on faults that cannot directly be detected from current sensor values but require inference from a sequence of time-varying sensor values. Each algorithm provides an independent improvement over the basic approach. These improvements are not mutually exclusive, and the algorithms may be combined to suit the application domain. All the approaches presented require dynamic models representing the behavior of each of the fault and operational states. These models can be built from analytical models of the robot dynamics, data from simulation, or from the real robot. All the approaches presented detect faults from a finite number of known fault conditions, although there may potentially be a very large number of these faults.

A model-based approach to prognostics and health management for flight control actuators

Abstract: Impact technologies have developed a robust modeling paradigm for actuator fault detection and failure prediction. This model-based approach to prognostics and health management (PHM) applies physical modeling and advanced parametric identification techniques, along with fault detection and failure prediction algorithms, in order to predict the time-to-failure for each of the critical, competitive failure modes within the system. Advanced probabilistic fusion strategies are also leveraged to combine both collaborative and competitive sources of evidence, thus producing more reliable health state information. These algorithms operate only on flight control command/response data. This approach for condition-based maintenance provides reliable early detection of developing faults. As an advantage over 'black-box' health-monitoring schemes, faults and failure modes are traced back to physically meaningful system parameters, providing the maintainer with invaluable diagnostic and prognostic information. The developed model-based reasoner was validated and demonstrated on an electromechanical actuator (EMA) provided by Moog, Inc.

Models for bearing damage detection in induction motors using stator current monitoring

Abstract: This paper describes new models for the influence of rolling-element bearing faults on induction motor stator current. Bearing problems are one major cause for drive failures. Their detection is possible by vibration monitoring of characteristic bearing frequencies. As it is possible to detect other machine faults by monitoring the stator current, a great interest exists in applying the same method for bearing fault detection. After a presentation of the existing fault model, a new detailed approach is proposed. It is based on two effects of a bearing fault: the introduction of a particular radial rotor movement and load torque variations caused by the bearing fault. The theoretical study results in new expressions for the stator current frequency content. Experimental tests with artificial and realistic bearing damage were conducted by measuring vibration, torque and stator current. The obtained results by spectral analysis of the measured quantities validate the proposed theoretical approach.

Induction machine fault detection using SOM-based RBF neural networks

Abstract: A radial-basis-function (RBF) neural-network-based fault detection system is developed for performing induction machine fault detection and analysis. Four feature vectors are extracted from power spectra of machine vibration signals. The extracted features are inputs of an RBF-type neural network for fault identification and classification. The optimal network architecture of the RBF network is determined automatically by our proposed cell-splitting grid algorithm. This facilitates the conventional laborious trial-and-error procedure in establishing an optimal architecture. In this paper, the proposed RBF machine fault diagnostic system has been intensively tested with unbalanced electrical faults and mechanical faults operating at different rotating speeds. The proposed system is not only able to detect electrical and mechanical faults, but the system is also able to estimate the extent of faults.

Architecture and techniques for diagnosing faults in IEEE 802.11 infrastructure networks

Abstract: The wide-scale deployment of IEEE 802.11 wireless networks has generated significant challenges for Information Technology (IT) departments in corporations. Users frequently complain about connectivity and performance problems, and network administrators are expected to diagnose these problems while managing corporate security and coverage. Their task is particularly difficult due to the unreliable nature of the wireless medium and a lack of intelligent diagnostic tools for determining the cause of these problems.This paper presents an architecture for detecting and diagnosing faults in IEEE 802.11 infrastructure wireless networks. To the best of our knowledge, ours is the first paper to address fault diagnostic issues for these networks. As part of our architecture, we propose and evaluate a novel technique called Client Conduit, which enables boot-strapping and fault diagnosis of disconnected clients. We describe techniques for analyzing performance problems faced in a wireless LAN deployment. We also present an approach for detecting unauthorized access points. We have built a prototype of our fault diagnostic architecture on the Windows operating system using off-the-shelf IEEE 802.11 cards. The initial results show that our mechanisms are effective; furthermore, they impose low overheads when clients are not experiencing problems.

On the robust fault detection via a sliding mode disturbance observer

Abstract: This paper deals with robust fault detection for non-linear systems. This problem is usually solved by designing an observable subsystem which is only affected by the fault and not by the control and disturbance inputs. However, such a subsystem may not exist so that the so-called fundamental problem of residual generation (FPRG) is not solvable. The aim of the present paper is to design a fault detection filter when the conditions for the existence of a solution to the non-linear FPRG are not satisfied. Our approach is made in a geometric context. Under some decoupling assumptions, the design of sliding mode observers allows us to reconstruct the disturbance inputs and then to generate an effective residual. An illustrative example is given throughout the paper.

Fault detection and isolation for hybrid systems using structured parity residuals

Abstract: Many physical systems are hybrid which means that both continuous and discrete states influence their dynamic behavior. The time evolution of the continuous states is constrained by vector fields that change due to internal or external discrete events. This paper is concerned with FDI (fault detection and isolation) for this kind of system. Two types of faults may be considered for hybrid dynamical systems depending on the component of the model that is affected by faults that may affect the current mode behavior or may affect the trajectory of the discrete evolution. The general principle of model-based FDI algorithms is to check the consistency of the known signals (inputs and outputs) w.r.t. a model. Parity residuals are special signals that reflect that consistency. As a direct consequence, these residuals may be used to detect faults in a given mode. Moreover, under the hypothesis that all modes are discernable, on-line mode identification is also possible which leads to diagnose the faults affecting the discrete sequence. In this paper, necessary and sufficient conditions are derived to guarantee the discernability between two modes and the complete FDI methodology, using parity residuals, is described.

Fault Detection, Isolation, and Diagnosis in Multihop Wireless Networks

Abstract: Network management in multihop wireless networks is key to efficient and reliable network operation. In this paper, we focus on a part of the general network management problem, namely fault detection, isolation, and diagnosis. We propose a system that employs online trace-driven simulation as a diagnostic tool for detecting faults and performing root cause analysis. We apply our system to diagnose faults such as packet dropping, link congestion, MAC misbehavior, and external noise, and show that it yields reasonably accurate results. In addition, we show that our system can be used to evaluate alternative network and node configurations to improve performance of on-going long-lived flows. Moreover, our technique is general enough to be applied in other wireless and wireline network management system.

A new PMU-based fault detection/location technique for transmission lines with consideration of arcing fault discrimination-part I: theory and algorithms

Abstract: A new fault detection/location technique with consideration of arcing fault discrimination based on phasor measurement units for extremely high voltage/ultra-high voltage transmission lines is presented in this two-paper set. Part I of this two-paper set is mainly aimed at theory and algorithm derivation. The proposed fault detection technique for both arcing and permanent faults is achieved by a combination of a fault detection index |M| and a fault location index |D|, which are obtained by processing synchronized fundamental phasors. One is to detect the occurrence of a fault and the other is to distinguish between in-zone and out-of-zone faults. Furthermore, for discriminating between arcing and permanent faults, the proposed technique estimates the amplitude of arc voltage by least error squares method through the measured synchronized harmonic phasors caused by the nonlinear arc behavior. Then, the discrimination will be achieved by comparing the estimated amplitude of arc voltage to a given threshold value. In addition, in order to eliminate the error caused by exponentially decaying dc offset on the computations of fundamental and harmonic phasors, an extended discrete Fourier transform algorithm is also presented.