Showing papers on "Stuck-at fault published in 2012"

Short-Circuit and Ground Fault Analyses and Location in VSC-Based DC Network Cables

Abstract: The application of high-power voltage-source converters (VSCs) to multiterminal dc networks is attracting research interest. The development of VSC-based dc networks is constrained by the lack of operational experience, the immaturity of appropriate protective devices, and the lack of appropriate fault analysis techniques. VSCs are vulnerable to dc-cable short-circuit and ground faults due to the high discharge current from the dc-link capacitance. However, faults occurring along the interconnecting dc cables are most likely to threaten system operation. In this paper, cable faults in VSC-based dc networks are analyzed in detail with the identification and definition of the most serious stages of the fault that need to be avoided. A fault location method is proposed because this is a prerequisite for an effective design of a fault protection scheme. It is demonstrated that it is relatively easy to evaluate the distance to a short-circuit fault using voltage reference comparison. For the more difficult challenge of locating ground faults, a method of estimating both the ground resistance and the distance to the fault is proposed by analyzing the initial stage of the fault transient. Analysis of the proposed method is provided and is based on simulation results, with a range of fault resistances, distances, and operational conditions considered.

Fault Analysis and Traveling-Wave Protection Scheme for Bipolar HVDC Lines

Abstract: Explicit fault analysis is the basis of protection for the bipolar HVDC Line. The characteristics of the initial values of traveling waves under various internal fault conditions are investigated on the basis of the symmetrical component analysis. The criteria of fault classification and faulty-pole selection are put forward based on the zero- and positive-sequence backward traveling waves, and an integrated traveling wave-based protection scheme is proposed. The simulations based on real-time digital simulation show that the proposed scheme can detect faults rapidly, determine the fault type effectively, and select the faulty pole correctly.

Method for Detecting an Open-Switch Fault in a Grid-Connected NPC Inverter System

Abstract: This paper proposes a fault-detection method for an open-switch fault in the switches of grid-connected neutral-point-clamped inverter systems. The proposed method can not only detect the fault condition but also identify the location of the faulty switch. In the proposed method, which is designed by incorporating a simple switching control in the conventional method, the fault condition is detected on the basis of the radius of the Concordia current pattern, and the location of the faulty switch can be identified. By using the proposed method, it is possible to detect the open-switch fault and identify the faulty switch within two fundamental periods, without using additional sensors or performing complex calculations. Simulations and experiments are carried out to confirm the reliability of the proposed fault-detection method.

Relyzer: exploiting application-level fault equivalence to analyze application resiliency to transient faults

Abstract: Future microprocessors need low-cost solutions for reliable operation in the presence of failure-prone devices. A promising approach is to detect hardware faults by deploying low-cost monitors of software-level symptoms of such faults. Recently, researchers have shown these mechanisms work well, but there remains a non-negligible risk that several faults may escape the symptom detectors and result in silent data corruptions (SDCs). Most prior evaluations of symptom-based detectors perform fault injection campaigns on application benchmarks, where each run simulates the impact of a fault injected at a hardware site at a certain point in the application's execution (application fault site). Since the total number of application fault sites is very large (trillions for standard benchmark suites), it is not feasible to study all possible faults. Previous work therefore typically studies a randomly selected sample of faults. Such studies do not provide any feedback on the portions of the application where faults were not injected. Some of those instructions may be vulnerable to SDCs, and identifying them could allow protecting them through other means if needed.This paper presents Relyzer, an approach that systematically analyzes all application fault sites and carefully picks a small subset to perform selective fault injections for transient faults. Relyzer employs novel fault pruning techniques that prune faults that need detailed study by either predicting their outcomes or showing them equivalent to other faults. We find that Relyzer prunes about 99.78% of the total faults across twelve applications studied here, reducing the faults that require detailed simulation by 3 to 5 orders of magnitude for most of the applications. Fault injection simulations on the remaining faults can identify SDC causing faults in the entire application. Some of Relyzer's techniques rely on heuristics to determine fault equivalence. Our validation efforts show that Relyzer determines fault outcomes with 96% accuracy, averaged across all the applications studied here.

Data-driven adaptive observer for fault diagnosis

Abstract: Published version of an article from the journal: Mathematical Problems in Engineering. Also available from the publisher:http://dx.doi.org/10.1155/2012/832836

PMU-Based Fault Location Using Voltage Measurements in Large Transmission Networks

Abstract: This paper presents a general fault-location method for large transmission networks which uses phasor measurement unit (PMU) voltage measurements where the injected current at a fault point can be calculated by using the voltage change and its relevant transfer impedance on any bus. A two-stage fault-location optimization model is proposed, along with defining a matching degree index. The first stage is the fault region identification stage, which uses the matching degree index to determine the suspicious fault region in order to reduce the search area. The second stage is used to identify the exact fault line and fault distance. A method to determine optimal PMU placement is also proposed in this paper. Case studies verify that the proposed fault-location algorithm and optimal PMU placement scheme can locate faults in large transmission networks quickly and accurately without requiring fault-type classification or fault phase selection.

A Transient Protection Scheme for HVDC Transmission Line

Abstract: The relation between the parameters of dc transmission line and the variation of transient energy has been analyzed under various fault conditions in this paper. According to that, a new transient energy protective scheme is proposed. It is developed based on the distributed parameter line model in which the transient energy distribution over the line can be obtained from the voltage and current measurements at both terminals and the fault can be recognized from the calculated value simply. The test system is modeled based on the CIGRE benchmark and considered the distributed parameters of the dc transmission line. Comprehensive test studies show that the performance of transient energy protection scheme is encouraging. It can not only identify internal fault and external faults correctly and quickly, but can also respond to the high ground resistance fault. Finally, two main factors, including fault resistance and transmission distance, that affect the performance of the protection are also discussed.

Fault Diagnosis and Fault-Tolerant Control in Linear Drives Using the Kalman Filter

Abstract: In this paper, we consider the fault diagnosis and fault-tolerant problem for a linear drive system subject to system noise. First, we propose a residual generator based on the Kalman filter, which can be used to detect if a failure occurs. Second, two Kalman filters are designed to diagnose the fault type. Third, when a fault is diagnosed, the fault-tolerant control is used to accommodate this failure. Finally, the proposed method is tested in a real linear drive system.

Accurate Single-Phase Fault-Location Method for Transmission Lines Based on K-Nearest Neighbor Algorithm Using One-End Voltage

Abstract: In this paper, some useful features are extracted from voltage signals measured at one terminal of the transmission line, which are highly efficient for accurate fault locating. These features are the amplitude of harmonic components, which are extracted after fault inception through applying discrete Fourier transform on one cycle of three-phase voltage signals and then are normalized by a transformation. In this paper, the location of single-line-to-ground faults as the most probable type of fault in the transmission networks is considered. The SLG fault locator, which is designed based on the simple algorithm of k-nearest neighbor (k-NN) in regression mode, estimates the location of fault related to the new input pattern based on existing available patterns. The proposed approach only needs the measured data from one terminal; hence, data communication between both ends of the line and synchronization are not required. In addition, current signals are not used; therefore, the proposed approach is immune against current-transformer saturation and its related errors. Tests conducted on an untransposed transmission line indicate that the proposed fault locator has accurate performance despite simultaneous changes in fault location, fault inception angle, fault resistance, and magnitude and direction of load current.

Fault Detection Isolation and Estimation in a Vehicle Steering System

Abstract: Recently, a bond-graph-based fault detection and isolation (FDI) framework has been developed with a new concept of global analytical redundancy relations (GARRs) (Low, Wang, Arogeti, and Luo, 2009, 2010; Low, Wang, Arogeti, and Zhang, 2010). This new concept allows the fault diagnosis for hybrid systems which consist of both continuous dynamics and discrete modes. A failure of a safety critical system such as the steering system of an automated guided vehicle may cause severe damage. Such failure can be avoided by an early detection and estimation of faults. In this paper, the newly developed FDI method is studied in details using an electrohydraulic steering system of an electric vehicle. The steering system and faults are modeled as a hybrid dynamic system by the hybrid bond graph (HBG) modeling technique. GARRs are then derived systematically from the HBG model with a specific causality assignment. Fault detection, isolation, and estimation are applied, experimental setup is described, and results are discussed.

A New Method for Power Converter Fault Diagnosis in SRM Drives

Abstract: This paper presents a new fault diagnostic technique applied to switched reluctance motor drives, based on the analysis of the power converter supply current. A fault is detected when the measured amplitude of the dc bus current differs from its expected amplitude, assuming normal operating conditions. The information about phase currents amplitudes and the control commands of all power switches permit to easily estimate the amplitude of the power converter supply current, since an asymmetric bridge converter is used. Simulation and experimental results are presented. Open- and short-circuit fault occurrences in the converter power switches are considered and analyzed. The proposed technique can early detect these fault occurrences and can also identify the affected motor phase. In almost all situations, the faulty element is also identified. An early fault diagnosis, with an accurate fault identification, is of a paramount importance since it permits the early adoption of fault-tolerant procedures that minimize the fault impact on the machine operation.

Short-Circuit Fault Protection Strategy for High-Power Three-Phase Three-Wire Inverter

Abstract: This paper proposes a four-stage fault protection scheme against the short-circuit fault for the high-power three-phase three-wire combined inverter to achieve high reliability. The short-circuit fault on the load side is the focus of this paper, and the short-circuit fault of switching devices is not involved. Based on the synchronous rotating frame, the inverter is controlled as a voltage source in the normal state. When a short-circuit fault (line-to-line fault or balanced three-phase fault) occurs, the hardware-circuit-based hysteresis current control strategy can effectively limit the output currents and protect the switching devices from damage. In the meantime, the software controller detects the fault and switches to the current controlled mode. Under the current controlled state, the inverter behaves as a current source until the short-circuit fault is cleared by the circuit breaker. After clearing the fault, the output voltage recovers quickly from the current controlled state. Therefore, the selective protection is realized and the critical loads can be continuously supplied by the inverter. The operational principle, design consideration, and implementation are discussed in this paper. The simulation and experimental results are provided to verify the validity of theoretical analysis.

Effects of the fault inception angle in fault-induced transients

Abstract: The analysis of fault-induced transients in three-phase overhead transmission lines can provide extensive information about the fault type, detection, location, direction and sustained time in satisfactory agreement with real application in protective relays These transients depend on the system topology, load condition and the fault parameters, such as the fault type, resistance, inception angle and location This study addresses the fault inception angle effects in the energies of the fault-induced transients in both voltages and currents by means of the wavelet coefficient energy analysis at the first three wavelet scales, in which a generic energy equation regarding the fault-induced transients as a function of the fault inception angle in all kinds of faults was established

Fault Detection and Classification in Plasma Etch Equipment for Semiconductor Manufacturing $e$ -Diagnostics

Abstract: A method of fault detection and classification (FDC) for semiconductor manufacturing equipment e-diagnostics using equipment data is presented. Detecting faulty processes, identifying any anomaly at their onsets, and rapidly classifying the root cause of the fault are crucial for maximizing equipment utilization in current semiconductor manufacturing; however, tool data acquired from production equipment contains much information that is often challenging to analyze due to its sheer volume and complexity. In this paper, modular neural network (MNN) modeling is presented as a method for fault detection modeling in plasma etching. Based on the result from the MNN modeling, a tool data set is grouped according to its related subsystems, and FDC is performed using Dempster-Shafer (D-S) theory to address the uncertainty associated with fault diagnosis. Subsystem level fault detections, such as radio frequency (RF) power source module, RF power bias module, gas delivery module, and process chamber module, are presented by combining related parameters, and successful fault detection is achieved. The evidential reasoning of RF probe is also beneficial for the detection of chamber leak simulation, and the classification of fault is made by further investigating voltage signal of RF probe. Successful fault detection in subsystem level with zero missed alarms was demonstrated using D-S theory of evidential reasoning, and the classification for finding root cause of the fault is presented in the chamber leak fault simulation. We realized that successful FDC can be accomplished by combining various related information and by incorporating engineering expert knowledge.

Sensor fault estimation and compensation for time-delay switched systems

Abstract: In this article, the problems of sensor fault estimation and compensation approaches for time-delay switched systems are investigated based on a switched descriptor observer approach. First, a novel time-delay switched descriptor state observer is proposed to estimate both the state and sensor fault. The proposed observer technique is also extended to systems with nonlinearities. Then, based on the estimation of the sensor fault, an efficient fault-tolerant operation can be realised via sensor fault compensation. Finally, an example is given to show the efficiency of the developed techniques.

Active fault-tolerant control of sampled-data nonlinear distributed parameter systems

Abstract: SUMMARY This work presents an integrated fault detection and fault-tolerant control architecture for spatially distributed systems described by highly dissipative systems of nonlinear partial differential equations with actuator faults and sampled measurements. The architecture consists of a family of nonlinear feedback controllers, observer-based fault detection filters that account for the discrete measurement sampling, and a switching law that reconfigures the control actuators following fault detection. An approximate finite-dimensional model that captures the dominant dynamics of the infinite-dimensional system is embedded in the control system to provide the controller and fault detection filter with estimates of the measured output between sampling instances. The model state is then updated using the actual measurements whenever they become available from the sensors. By analyzing the behavior of the estimation error between sampling times and exploiting the stability properties of the compensated model, a sufficient condition for the stability of the sampled-data nonlinear closed-loop system is derived in terms of the sampling rate, the model accuracy, the controller design parameters, and the spatial placement of the control actuators. This characterization is used as the basis for deriving appropriate rules for fault detection and actuator reconfiguration. Singular perturbation techniques are used to analyze the implementation of the developed architecture on the infinite-dimensional system. The results are demonstrated through an application to the problem of stabilizing the zero solution of the Kuramoto–Sivashinsky equation. Copyright © 2011 John Wiley & Sons, Ltd.

Fast single-FPGA fault injection platform

Abstract: Evaluating the resilience of a given circuit against adverse effects, such as radiation-induced single event upsets, is a complex and frequently time-demanding task. For Field Programmable Gate Arrays (FPGAs), this task has the additional complexity of accounting for faults affecting the configuration memory. For this reason, several works propose techniques to inject and evaluate faults affecting configuration bits. In this work, we propose a novel platform which requires a single FPGA to perform the fault injection, to apply input vectors and to evaluate the correctness of the outputs. It can evaluate complex fault models, such as multiple bit errors that are caused by a single bit flip. Furthermore, it occupies a small portion of the device resources and works at a very high speed, being able to inject and remove a fault in under 10μ8.

Runtime asynchronous fault tolerance via speculation

Abstract: Transient faults are emerging as a critical reliability concern in modern microprocessors. Redundant hardware solutions are commonly deployed to detect transient faults, but they are less flexible and cost-effective than software solutions. However, software solutions are rendered impractical because of high performance overheads. To address this problem, this paper presents Runtime Asynchronous Fault Tolerance via Speculation (RAFT), the fastest transient fault detection technique known to date. Serving as a layer between the application and the underlying platform, RAFT automatically generates two symmetric program instances from a program binary. It detects transient faults in a non-invasive way and exploits high-confidence value speculation to achieve low runtime overhead. Evaluation on a commodity multicore system demonstrates that RAFT delivers a geomean performance overhead of 2.83% on a set of 30 SPEC CPU benchmarks and STAMP benchmarks. Compared with existing transient fault detection techniques, RAFT exhibits the best performance and fault coverage, without requiring any change to the hardware or the software applications.

Online Fault Diagnosis for Power Transmission Networks Using Fuzzy Digraph Models

Abstract: This paper presents an effective method for online fault diagnosis, in power transmission networks, based on fuzzy directed graph (digraph) models. When the relationships between fault sections and protective devices are represented by a digraph model, the possible fault sections can be traced through the proposed fuzzy inference algorithm. The main features of the proposed method include its ease of implementation, parallel information-processing capability, and uncertainty reasoning capabilities. Upon testing in two systems, the proposed method was demonstrated to be feasible and yielded satisfactory results, even with complex fault scenarios.

Small-delay-fault ATPG with waveform accuracy

Abstract: The detection of small-delay faults is traditionally performed by sensitizing transitions on a path of sufficient length from an input to an output of the circuit going through the fault site. While this approach allows efficient test generation algorithms, it may result in false positives and false negatives as well, i.e. undetected faults are classified as detected or detectable faults are classified as undetectable. We present an automatic test pattern generation algorithm which considers waveforms and their propagation on each relevant line of the circuit. The model incorporates individual delays for each gate and filtering of small glitches. The algorithm is based on an optimized encoding of the test generation problem by a Boolean satisfiability (SAT) instance and is implemented in the tool WaveSAT. Experimental results for ISCAS-85, ITC-99 and industrial circuits show that no known definition of path sensitization can eliminate false positives and false negatives at the same time, thus resulting in inadequate small-delay fault detection. WaveSAT generates a test if the fault is testable and is also capable of automatically generating a formal redundancy proof for undetectable small-delay faults; to the best of our knowledge this is the first such algorithm that is both scalable and complete.

New Fault-Based Side-Channel Attack Using Fault Sensitivity

Abstract: This paper proposes a new fault-based attack called fault sensitivity analysis (FSA) attack. In the FSA attack, fault injections are used to test out the sensitive information leakage called fault sensitivity. Fault sensitivity means the critical fault injection intensity that corresponds to the threshold between devices' normal and abnormal behaviors. We demonstrate that without using the values of the faulty outputs, attackers can obtain the information of the secret key based on the data-dependency of the collected fault sensitivity data. This paper explains the successful FSA attacks against three Advanced Encryption Standard (AES) hardware implementations, where two of them are resistant to the differential fault analysis. This paper also discusses the countermeasures against the proposed FSA attacks.

Integrated fault location system for power distribution feeders

Abstract: A fast, accurate fault location solution for power distribution feeders enables utility companies to clear a fault quicker and reduce the outage duration. Traditional impedance-based fault location methods assume that all feeder sections have the same impedance characteristics. This assumption introduces errors on feeders that have branches and line sections with different conductor types and tower configurations. Automated meter reading and trouble call systems provide fault location estimation only for faults that are isolated by field devices. This paper presents a new impedance-based method that estimates all possible fault locations by using a detailed feeder model and fault event reports, complemented with information from other intelligent field devices. Field test results show significant benefits from using the new method over the traditional method. This paper also describes a real-world fault location system that implements the reported method and provides automated fault location estimation within one minute after the fault occurs.

Fault detection and diagnosis of induction motors based on hidden Markov model

Abstract: Accurate fault detection and diagnosis in complex systems is necessary for economic and security reasons. In this paper, we present a novel approach for fault detection and diagnosis based on Hidden Markov Models. This approach uses pattern recognition combining motor current signature analysis and multiple features extracted from transformations made on current and voltage signals in order to build the representation space. If the representation space is well chosen, each operating mode can be represented as a class. A hidden Markov model is then designed for each class and used as classifier for the detection and diagnosis of faults. The proposed approach is tested on an induction motor of 5.5 Kw with bearing failures and broken rotor bars. Further, the effectiveness of this approach is compared with a neural-network-based approach. The experimental results prove the efficiency of the hidden Markov model-based approach in condition monitoring of electrical machines.

Fault detection and isolation in manufacturing systems with an identified discrete event model

Abstract: In this article a generic method for fault detection and isolation FDI in manufacturing systems considered as discrete event systems DES is presented. The method uses an identified model of the closed-loop of plant and controller built on the basis of observed fault-free system behaviour. An identification algorithm known from literature is used to determine the fault detection model in form of a non-deterministic automaton. New results of how to parameterise this algorithm are reported. To assess the fault detection capability of an identified automaton, probabilistic measures are proposed. For fault isolation, the concept of residuals adapted for DES is used by defining appropriate set operations representing generic fault symptoms. The method is applied to a case study system.

Location of faults generating short-duration voltage variations in distribution systems regions from records captured at one point and decomposed into damped sinusoids

Abstract: This study investigates the detection and localisation of faults provoking short-duration voltage variations - sags (dips) and swells - in small power distribution networks. It aims to accomplish those tasks by capturing fault records, voltage and current waveforms, at just one point in the system, the substation. The main objective is to classify the fault type and locate the fault origin occurring in a given region of a power delivery network. For that purpose, fault inception is triggered by a sensitive phase-locked loop. Then, the captured signals are decomposed using damped sinusoids of arbitrary temporal support by means of an adaptive decomposition algorithm. Subsets of the parameters defining the damped sinusoids are used for classifying the fault type and indicating the fault location. The fault-type classification is obtained by using support vector machines, whereas the fault location is obtained by means of an artificial neural network. The simulation results for a simple but actual power distribution system with three possible places for fault occurrence are presented. The exact fault-type classifications were obtained while a correct localisation of 85- of the faults was accomplished.