Showing papers on "Fault detection and isolation published in 2007"

Locating and Isolating DC Faults in Multi-Terminal DC Systems

Abstract: A VSC-MTDC (multi-terminal dc) system consists of voltage-source converters (VSCs) connected to a dc network at their dc terminals. The MTDC is most vulnerable to a dc fault which paralyses all the VSCs until the dc fault is cleared. As dc circuit breakers are expensive, this paper proposes a solution based on extinguishing the dc fault current by opening all the ac-circuit breakers (ac-CBs) which the VSCs are already equipped with on the ac-sides. However, it is necessary to identify which dc line is the faulted line (in case it is a permanent fault) so that it can be isolated by fast dc switches (which are much more economical than the dc circuit breakers), prior to restoring the MTDC system by re-closing all the ac-CBs. This paper presents the handshaking method, which locates and isolates the faulted dc line and restores the MTDC without telecommunication.

Predicting Faults from Cached History

Abstract: We analyze the version history of 7 software systems to predict the most fault prone entities and files. The basic assumption is that faults do not occur in isolation, but rather in bursts of several related faults. Therefore, we cache locations that are likely to have faults: starting from the location of a known (fixed) fault, we cache the location itself, any locations changed together with the fault, recently added locations, and recently changed locations. By consulting the cache at the moment a fault is fixed, a developer can detect likely fault-prone locations. This is useful for prioritizing verification and validation resources on the most fault prone files or entities. In our evaluation of seven open source projects with more than 200,000 revisions, the cache selects 10% of the source code files; these files account for 73%-95% of faults - a significant advance beyond the state of the art.

A Survey of Fault Management in Wireless Sensor Networks

Abstract: Wireless sensor networks are resource-constrained self-organizing systems that are often deployed in inaccessible and inhospitable environments in order to collect data about some outside world phenomenon. For most sensor network applications, point-to-point reliability is not the main objective; instead, reliable event-of-interest delivery to the server needs to be guaranteed (possibly with a certain probability). The nature of communication in sensor networks is unpredictable and failure-prone, even more so than in regular wireless ad hoc networks. Therefore, it is essential to provide fault tolerant techniques for distributed sensor applications. Many recent studies in this area take drastically different approaches to addressing the fault tolerance issue in routing, transport and/or application layers. In this paper, we summarize and compare existing fault tolerant techniques to support sensor applications. We also discuss several interesting open research directions.

Fault Detection Using the k-Nearest Neighbor Rule for Semiconductor Manufacturing Processes

Abstract: It has been recognized that effective fault detection techniques can help semiconductor manufacturers reduce scrap, increase equipment uptime, and reduce the usage of test wafers. Traditional univariate statistical process control charts have long been used for fault detection. Recently, multivariate statistical fault detection methods such as principal component analysis (PCA)-based methods have drawn increasing interest in the semiconductor manufacturing industry. However, the unique characteristics of the semiconductor processes, such as nonlinearity in most batch processes, multimodal batch trajectories due to product mix, and process steps with variable durations, have posed some difficulties to the PCA-based methods. To explicitly account for these unique characteristics, a fault detection method using the k-nearest neighbor rule (FD-kNN) is developed in this paper. Because in fault detection faults are usually not identified and characterized beforehand, in this paper the traditional kNN algorithm is adapted such that only normal operation data is needed. Because the developed method makes use of the kNN rule, which is a nonlinear classifier, it naturally handles possible nonlinearity in the data. Also, because the FD-kNN method makes decisions based on small local neighborhoods of similar batches, it is well suited for multimodal cases. Another feature of the proposed FD-kNN method, which is essential for online fault detection, is that the data preprocessing is performed automatically without human intervention. These capabilities of the developed FD-kNN method are demonstrated by simulated illustrative examples as well as an industrial example.

An Analysis of Performance Interference Effects in Virtual Environments

Abstract: Virtualization is an essential technology in modern datacenters Despite advantages such as security isolation, fault isolation, and environment isolation, current virtualization techniques do not provide effective performance isolation between virtual machines (VMs) Specifically, hidden contention for physical resources impacts performance differently in different workload configurations, causing significant variance in observed system throughput To this end, characterizing workloads that generate performance interference is important in order to maximize overall utility In this paper, we study the effects of performance interference by looking at system-level workload characteristics In a physical host, we allocate two VMs, each of which runs a sample application chosen from a wide range of benchmark and real-world workloads For each combination, we collect performance metrics and runtime characteristics using an instrumented Ken hypervisor Through subsequent analysis of collected data, we identify clusters of applications that generate certain types of performance interference Furthermore, we develop mathematical models to predict the performance of a new application from its workload characteristics Our evaluation shows our techniques were able to predict performance with average error of approximately 5%

Overcurrent Protection on Voltage-Source-Converter-Based Multiterminal DC Distribution Systems

Abstract: This paper proposes a protection scheme which utilizes modern voltage-source converters as fast-acting current-limiting circuit breakers. This paper investigates the main challenges of detecting and localizing a fault, and interrupting it as quickly as possible in a multiterminal dc system. A system protection scheme consisting of smart relays associated with converters has been developed. The protection relays monitor local quantities to detect and isolate disturbances/faults. It is shown that overcurrent-based schemes can be adopted for these relays to meet the fast response requirements. The effectiveness of the proposed protection scheme is illustrated through simulations

A High-Resolution Frequency Estimation Method for Three-Phase Induction Machine Fault Detection

Abstract: Fault detection in alternating-current electrical machines that is based on frequency analysis of stator current has been the interest of many researchers. Several frequency estimation techniques have been developed and are used to help the induction machine fault detection and diagnosis. This paper presents a technique to improve the fault detection technique by using the classical multiple signal classification (MUSIC) method. This method is a powerful tool that extracts meaningful frequencies from the signal, and it has been widely used in different areas, which include electrical machines. In the proposed application, the fault sensitive frequencies have to be found in the stator current signature. They are numerous in a given frequency range, and they are affected by the signal-to-noise ratio. Then, the MUSIC method takes a long computation time to find many frequencies by increasing the dimension of the autocorrelation matrix. To solve this problem, an algorithm that is based on zooming in a specific frequency range is proposed with MUSIC in order to improve the performances of frequency extraction. Moreover, the method is integrated as a part of MUSIC to estimate the frequency signal dimension order based on classification of autocorrelation matrix eigenvalues. The proposed algorithm has been applied to detect a rotor broken bar fault in a three-phase squirrel-cage induction machine under different loads and in steady-state condition.

Induction Machine Condition Monitoring Using Neural Network Modeling

Abstract: Condition monitoring is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. Model-based methods are efficient monitoring systems for providing warning and predicting certain faults at early stages. However, the conventional methods must work with explicit motor models, and cannot be applied effectively for vibration signal diagnosis due to their nonadaptation and the random nature of vibration signal. In this paper, an analytical redundancy method using neural network modeling of the induction motor in vibration spectra is proposed for machine fault detection and diagnosis. The short-time Fourier transform is used to process the quasi-steady vibration signals to continuous spectra for the neural network model training. The faults are detected from changes in the expectation of vibration spectra modeling error. The effectiveness of the proposed method is demonstrated through experimental results, and it is shown that a robust and automatic induction machine condition monitoring system has been produced

Fault Detection for Uncertain Fuzzy Systems: An LMI Approach

Abstract: This paper studies the problem of designing a robust fault-detection system for uncertain Takagi-Sugeno fuzzy models. The worst case fault sensitivity measure is formulated in terms of linear matrix inequalities. The existence of a robust fault detection system that guarantees i) the L2-gain from a fault signal to a residual signal greater than a prescribed value and ii) the L2-gain from an exogenous input to a residual signal less than a prescribed value is given in terms of the solvability of linear matrix inequalities. Numerical examples are used to illustrate the effectiveness of the proposed design techniques.

Adaptive Neural Fuzzy Inference System for the Detection of Inter-Turn Insulation and Bearing Wear Faults in Induction Motor

Abstract: The positive features of neural networks and fuzzy logic are combined together for the detection of stator inter-turn insulation and bearing wear faults in single-phase induction motor. The adaptive neural fuzzy inference systems (ANFISs) are developed for the detection of these two faults. These faults are created experimentally on a single-phase induction motor in the laboratory. The experimental data is generated for the five measurable parameters, viz, motor intakes current, speed, winding temperature, bearing temperature, and the noise of the machine. Earlier, the ANFIS fault detectors are trained for the two input parameters, i.e., speed and current, and the performance is tested. Later, the three remaining parameters are added and the five input ANFIS fault detector is trained and tested. It observed from the simulation results that the five input parameter system predicts more accurate results

Fault Diagnosis and Reconfiguration for Multilevel Inverter Drive Using AI-Based Techniques

Abstract: A fault diagnostic and reconfiguration method for a cascaded H-bridge multilevel inverter drive (MLID) using artificial-intelligence-based techniques is proposed in this paper. Output phase voltages of the MLID are used as diagnostic signals to detect faults and their locations. It is difficult to diagnose an MLID system using a mathematical model because MLID systems consist of many switching devices and their system complexity has a nonlinear factor. Therefore, a neural network (NN) classification is applied to the fault diagnosis of an MLID system. Multilayer perceptron networks are used to identify the type and location of occurring faults. The principal component analysis is utilized in the feature extraction process to reduce the NN input size. A lower dimensional input space will also usually reduce the time necessary to train an NN, and the reduced noise can improve the mapping performance. The genetic algorithm is also applied to select the valuable principal components. The proposed network is evaluated with simulation test set and experimental test set. The overall classification performance of the proposed network is more than 95%. A reconfiguration technique is also proposed. The proposed fault diagnostic system requires about six cycles to clear an open-circuit or short-circuit fault. The experimental results show that the proposed system performs satisfactorily to detect the fault type, fault location, and reconfiguration.

Model-Based Diagnosis of Hybrid Systems

Abstract: Techniques for diagnosing faults in hybrid systems that combine digital (discrete) supervisory controllers with analog (continuous) plants need to be different from those used for discrete or continuous systems. This paper presents a methodology for online tracking and diagnosis of hybrid systems. We demonstrate the effectiveness of the approach with experiments conducted on the fuel-transfer system of fighter aircraft

Time-series methods for fault detection and identification in vibrating structures

Abstract: An overview of the principles and techniques of time-series methods for fault detection, identification and estimation in vibrating structures is presented, and certain new methods are introduced. The methods are classified, and their features and operation are discussed. Their practicality and effectiveness are demonstrated through brief presentations of three case studies pertaining to fault detection, identification and estimation in an aircraft panel, a scale aircraft skeleton structure and a simple nonlinear simulated structure.

Model-based Fault Detection and Diagnosis of HVAC systems using Support Vector Machine method

Abstract: Preventive maintenance plays a very important role in the modern Heating, Ventilation and Air Conditioning (HVAC) systems for guaranteeing the thermal comfort, energy saving and reliability. Its key is a cost-effective Fault Detection and Diagnosis (FDD) method. To achieve this goal, this paper proposes a new method by combining the model-based FDD method and the Support Vector Machine (SVM) method. A lumped-parameter model of a single zone HVAC system is developed first, and then the characteristics of three major faults, including the recirculation damper stuck, cooling coil fouling/block and supply fan speed decreasing, are investigated by computer simulation. It is found that the supply air temperature, mixed air temperature, outlet water temperature and control signal are sensitive to the faults and can be selected as the fault indicators. Based on the variations of the system states under the normal and faulty conditions of different degrees, the faults can be detected efficiently by using the residual analysis method. Furthermore, a multi-layer SVM classifier is developed, and the diagnosis results show that this classifier is effective with high accuracy. As a result, the presented Model-Based Fault Detection and Diagnosis (MBFDD) method can help to maintain the health of the HVAC systems, reduce energy consumption and maintenance cost.

Distance Relay With Out-of-Step Blocking Function Using Wavelet Transform

Abstract: Out-of-step blocking function in distance relays is required to distinguish between a power swing and a fault. Speedy and reliable detection of symmetrical faults during power swings presents a challenge. This paper introduces wavelet transform to reliably and quickly detect power swings as well as detect any fault during a power swing. The total number of dyadic wavelet levels of voltage/current waveforms and the choice of particular levels for such detection are carefully studied. A logic block based on the wavelet transform is developed. The output of this block is combined with the output of the conventional digital distance relay to achieve desired performance during power swings. This integrated relay is extensively tested on a simulated system using PSCAD/ EMTDCreg software.

H/sub /spl infin// fault detection filter design for networked control systems modelled by discrete Markovian jump systems

Abstract: This paper deals with the design of robust fault detection for networked control systems with large transfer delays, in which it is impossible to totally decouple the fault effects from unknown inputs (including model uncertainties and external plant disturbances). First, we employ the multirate sampling method together with the augmented state matrix method to model the long random delay networked control systems as Markovian jump systems. Then, a Hinfin fault detection filter is designed based on the model developed. Through the appropriate choice of the filter gain, the filter is convergent if there is no disturbance in the system, meanwhile the effect of disturbances on the residual will satisfy a prescribed Hinfin performance. The problem of achieving satisfactory sensitivity of the residual to fault is formulated and its solution is given. Finally, a numerical example is presented to illustrate the effectiveness of the proposed techniques.

Configurable isolation: building high availability systems with commodity multi-core processors

Abstract: High availability is an increasingly important requirement for enterprise systems, often valued more than performance. Systems designed for high availability typically use redundant hardware for error detection and continued uptime in the event of a failure. Chip multiprocessors with an abundance of identical resources like cores, cache and interconnection networks would appear to be ideal building blocks for implementing high availability solutions on chip. However, doing so poses significant challenges with respect to error containment and faulty component replacement. Increasing silicon and transient fault rates with future technology scaling exacerbate the problem. This paper proposes a novel, cost-effective, architecture for high availability systems built from future multi-core processors. We propose a new chip multiprocessor architecture that provides configurable isolation for fault containment and component retirement, based upon cost-effective modifications to commodity designs. The design is evaluated for a state-of-the-art industrial fault model and the proposed architecture is shown to provide effective fault isolation and graceful degradation even when the failure rate is high.

Distributed Diagnosis of Place-Bordered Petri Nets

Abstract: This paper studies online fault detection and isolation of modular dynamic systems modeled as sets of place-bordered Petri nets. The common places among the set of Petri nets modeling a system capture coupling of various system components. The transitions are labeled by events, some of which are unobservable (i.e., not directly recorded by the sensors attached to the system). The events whose occurrence must be diagnosed have unobservable transition labels. These events model faults or other significant changes in the system state. The existing theory of diagnosis of discrete-event systems is extended in the context of the above model. The modular structure of the system is exploited by a distributed algorithm for fault diagnosis. A Petri net diagnoser is associated with every Petri net and the diagnosers communicate in real time during the diagnostic process when the token count of common places changes. A merge function is defined to combine the individual diagnoser states and recover the complete diagnoser state that would be obtained under a monolithic approach. Strategies that reduce the communication overhead are presented. The software implementation of the distributed algorithm is discussed. Note to Practitioners-In the last decade, monitoring, fault detection, and diagnosis methodologies based on the use of discrete-event models have been successfully used in a variety of technological systems ranging from document processing systems to intelligent transportation systems. This paper was motivated by the problem of fault diagnosis for modular (distributed) dynamic discrete-event systems (DES). As a DES modeling formalism, Petri nets offer potential advantages in terms of the distributed representation of the system and the ability to represent coupling of the system components. The systems studied in this paper are sets of modules coupled with each other through various system components and modeled using Petri nets. We present a distributed fault diagnosis algorithm which allows each module in the distributed system to diagnose its faults independently unless completion of a task requires the use of coupled components. In the case of coupling, modules communicate with each other to accurately diagnose the fault. The distributed fault diagnosis algorithm recovers the monolithic diagnosis information at the cost of communication and growing communication overhead. To mitigate that problem, we present an improved version of the algorithm that significantly reduces the communication overhead. Finally, we introduce the software toolbox (written in Matlab and integrated with AT&T Graphviz) and we present a case study of an example of a heating, ventilation, and air-conditioning system where we use the software tool for modeling and analyzing the system

Adaptive unknown input observer approach for aircraft actuator fault detection and isolation

Abstract: In this paper, an adaptive unknown input observer (UIO) approach is developed to detect and isolate aircraft actuator faults. In a multiple-model scheme, a bank of parallel observers are constructed, each of which is based on a model that describes the system in the presence of a particular actuator fault. The observers are constructed based on a modified form of the standard UIO to generate fault-dependant residual signals, such that when a model matches the system, the residual signal will be zero. Otherwise, the residual will be definitely non-zero and governed uniquely by the faulty signal. For locked actuators and loss of actuator effectiveness, in which the locked position and the reduced effectiveness are additional unknowns, we develop an adaptive scheme to estimate these unknown parameters. To the best of our knowledge, this is the first adaptive UIO presented. We prove that the proposed adaptive algorithms guarantee that both the residual signals and the estimation errors of the unknown parameters converge exponentially when a model matches the plant. By further designing a model-matching index, the fault can be isolated accurately. A condition for the approach is that for an nth order system, there must be n independent measurements available. This requirement limits the applicability of our proposed approach. The condition is certainly satisfied by all state-feedback control systems. However, for some other systems, extra efforts may be needed to increase the number of measurements. The method is applied to a linear model of the F-16 aircraft with controller. The results show that the approach is effective. Copyright © 2006 John Wiley & Sons, Ltd.

Modeling and experimental verification of high impedance arcing fault in medium voltage networks

Abstract: A high impedance arcing fault due to a leaning tree in medium voltage (MV) networks is modeled and experimentally verified. The fault is represented in two parts; an arc model and a high resistance. The arc is generated by a leaning tree towards the network conductor and the tree resistance limits the fault current. The arcing element is dynamically simulated using thermal equations. The arc model parameters and resistance values are determined using the experimental results. The fault behavior is simulated by the ATP/EMTP program, in which the arc model is realized using the universal arc representation. The experimental results have validated the system transient model. Discrete wavelet transform is used to extract the fault features and therefore localize the fault events. It is found that arc reignitions enhance fault detection when discrete wavelet transform is utilized

Non-Adaptive Fault Diagnosis for All-Optical Networks via Combinatorial Group Testing on Graphs

Abstract: We consider the problem of detecting failures for all-optical networks, with the objective of keeping the diagnosis cost low. Compared to the passive paradigm based on parity check in SONET, optical probing signals are sent proactively along lightpaths to probe their state of health and failure pattern is identified through the set of test results (i.e., probe syndromes). As an alternative to our previous adaptive approach where all the probes are sent sequentially, we consider in this work a non-adaptive approach where all the probes are sent in parallel. The design objective is to minimize the number of parallel probes, so as to keep network cost low. The non-adaptive fault diagnosis approach motivates a new technical framework that we introduce: combinatorial group testing with graph-based constraints. Using this framework, we develop several new probing schemes to detect network faults for all-optical networks with different topologies. The efficiency of our schemes often depends on the network topology; in many cases we can show that our schemes are optimal in minimizing the number of probes.

Fault Detection Mechanism for LED Backlighting

Abstract: A fault detection mechanism for a LED string comprising a plurality of serially connected LEDs, the fault detection mechanism comprising: a control circuitry; and a voltage measuring means, in communication with the control circuitry, arranged to measure the voltage drop across at least one LED of the LED string, the control circuitry being operable to: measure the voltage drop, via the voltage measuring means, at a plurality of times, compare at least two of the measured voltage drops, and in the event the comparison of the at least two voltage drops is indicative of one of a short circuit LED and an open circuit LED, output a fault indicator.