Showing papers on "Stuck-at fault published in 2017"

Evaluating and improving fault localization

Abstract: Most fault localization techniques take as input a faulty program, and produce as output a ranked list of suspicious code locations at which the program may be defective When researchers propose a new fault localization technique, they typically evaluate it on programs with known faults The technique is scored based on where in its output list the defective code appears This enables the comparison of multiple fault localization techniques to determine which one is better Previous research has evaluated fault localization techniques using artificial faults, generated either by mutation tools or manually In other words, previous research has determined which fault localization techniques are best at finding artificial faults However, it is not known which fault localization techniques are best at finding real faults It is not obvious that the answer is the same, given previous work showing that artificial faults have both similarities to and differences from real faults We performed a replication study to evaluate 10 claims in the literature that compared fault localization techniques (from the spectrum-based and mutation-based families) We used 2995 artificial faults in 6 real-world programs Our results support 7 of the previous claims as statistically significant, but only 3 as having non-negligible effect sizes Then, we evaluated the same 10 claims, using 310 real faults from the 6 programs Every previous result was refuted or was statistically and practically insignificant Our experiments show that artificial faults are not useful for predicting which fault localization techniques perform best on real faults In light of these results, we identified a design space that includes many previously-studied fault localization techniques as well as hundreds of new techniques We experimentally determined which factors in the design space are most important, using an overall set of 395 real faults Then, we extended this design space with new techniques Several of our novel techniques outperform all existing techniques, notably in terms of ranking defective code in the top-5 or top-10 reports

A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering Transient and Intermittent Faults

Abstract: Transient fault (TF) and intermittent fault (IF) of complex electronic systems are difficult to diagnose. As the performance of electronic products degrades over time, the results of fault diagnosis could be different at different times for the given identical fault symptoms. A dynamic Bayesian network (DBN)-based fault diagnosis methodology in the presence of TF and IF for electronic systems is proposed. DBNs are used to model the dynamic degradation process of electronic products, and Markov chains are used to model the transition relationships of four states, i.e., no fault, TF, IF, and permanent fault. Our fault diagnosis methodology can identify the faulty components and distinguish the fault types. Four fault diagnosis cases of the Genius modular redundancy control system are investigated to demonstrate the application of this methodology.

A Unified Analysis of the Fault Tolerance Capability in Six-Phase Induction Motor Drives

Abstract: The fault tolerance of electric drives is highly appreciated at industry for security and economic reasons, and the inherent redundancy of six-phase machines provides the desired fault-tolerant capability with no extra hardware. For this reason some recent research efforts have been focused on the fault-tolerant design, modeling, and control of six-phase machines. Nevertheless, a unified and conclusive analysis of the postfault capability of six-phase machine is still missing. This paper provides a full picture of the postfault derating in generic six-phase machines and a specific analysis of the fault-tolerant capability of the three mainstream six-phase induction machines (asymmetrical, symmetrical, and dual three phase). Experimental results confirm the theoretical post fault current limits and allow concluding, which is the best six-phase machine for each fault scenario and neutral arrangement.

Fault location and detection techniques in power distribution systems with distributed generation: A review

Abstract: Distribution systems are continuously exposed to fault occurrences due to various reasons, such as lightning strike, failure of power system components due to aging of equipment and human errors. These phenomena affect the system reliability and results in expensive repairs, lost of productivity and power loss to customers. Since fault is unpredictable, a fast fault location and isolation is required to minimize the impact of fault in distribution systems. Therefore, many methods have been developed since the past to locate and detect faults in distribution systems with distributed generation. The methods can be divided into two categories, conventional and artificial intelligence techniques. Conventional techniques include travelling wave method and impedance based method while artificial intelligence techniques include Artificial Neural Network (ANN), Support Vector Machine (SVM), Fuzzy Logic, Genetic Algorithm (GA) and matching approach. However, fault location using intelligent methods are challenging since they require training data for processing and are time consuming. In this paper, most of the techniques that have been developed since the past and commonly used to locate and detect faults in distribution systems with distributed generation are reviewed. Research works in fault location area, the working principles, advantages and disadvantages of past works related to each fault location technique are highlighted in this paper. Hence, from this review, the opportunities in fault location research area in power distribution system can be explored further.

A Pole-to-Pole Short-Circuit Fault Current Calculation Method for DC Grids

Abstract: This paper proposes a generic pole-to-pole short-circuit fault current calculation method for dc grids. The calculation procedure begins from the simplified RLC equivalent model of a single modular multilevel converter, and then the prefault matrices and faulted matrices are established and modified to calculate the dc fault currents of all the branches. The proposed approaches are validated by comparing with the electromagnetic transient (EMT) simulation results on PSCAD/EMTDC. Besides, two case studies showed that the calculation method can be easily used to evaluate the severity of a dc fault. Moreover, the calculation can be applied to select the parameters of a fault current limiter (to match the circuit breaker capacity. The main contributions of the proposed numerical calculation method are: 1) The proposed method is accurate and much more time efficient than the EMT simulations; 2) the proposed method can handle all kinds of dc grid networks including the ring, radial, and meshed topologies; and 3) the proposed method is applicable to dc grid with multiple dc voltage level areas connected with dc/dc converters.

Model-Based Fault Detection and Identification for Switching Power Converters

Abstract: We present the analysis, design, and experimental validation of a model-based fault detection and identification (FDI) method for switching power converters using a model-based state estimator approach. The proposed FDI approach is general in that it can be used to detect and identify arbitrary faults in components and sensors in a broad class of switching power converters. The FDI approach is experimentally demonstrated on a nanogrid prototype with a 380-V dc distribution bus. The nanogrid consists of four different switching power converters, including a buck converter, an interleaved boost converter, a single-phase rectifier, and a three-phase inverter. We construct a library of fault signatures for possible component and sensor faults in all four converters. The FDI algorithm successfully achieves fault detection in under 400 $\mu$ s and fault identification in under 10 ms for faults in each converter. The proposed FDI approach enables a flexible and scalable solution for improving fault tolerance and awareness in power electronics systems.

Neural Network-Based Model-Free Adaptive Fault-Tolerant Control for Discrete-Time Nonlinear Systems With Sensor Fault

Abstract: In this paper, the main focus is to cope with the fault detection and estimation (FDE) and fault-tolerant control (FTC) issues of nonlinear single input single output model-free system (MFS), while only the input/output data are utilized. First, in accordance with the pseudo-partial-derivative approach, the original system is transformed into a compact form dynamic linearization data model, in which only one parameter is employed. Second, an estimator is developed to detect the fault. A key highlight is the design of a time varying residual threshold. Moreover, an online neural network (NN) approximator is utilized to learn the unknown fault dynamics and an FTC strategy is reconstructed based on the optimality criterion. In contrast to the previous methods, the main features of the proposed method are as follows: 1) the fault related problem is solved for MFS; 2) the number of system parameters is largely reduced; and 3) NNs are utilized to establish a novel fault estimation scheme. Finally, a numerical simulation is provided to show the effectiveness of the proposed FDE and FTC strategy.

Open-Circuit IGBT Fault Detection and Location Isolation for Cascaded Multilevel Converters

Abstract: An open-circuit insulated-gate bipolar transistor fault detection technique for cascaded H-bridge (CHB) multilevel converters is presented in this paper. This technique, designed to be implemented independently for each CHB leg, utilizes one current sensor and one voltage sensor to monitor a leg's current and output voltage. Measured voltages are compared to expected voltages, and deviations are used to determine open-circuit fault locations based on the deviation's magnitude and current flow direction. Once potential fault locations have been identified, the fault location is systematically isolated and then verified, reducing the possibility of unnecessary corrective actions due to fault misidentification, e.g., an intermittent gate-misfiring fault being classified as an open-circuit fault. The proposed technique can be implemented for any number of cells, is independent of the pulse width modulation strategy used, and can be applied to symmetric and asymmetric CHB converters regardless of the cell input dc-source magnitudes utilized, i.e., cell input voltages are not required to be equal or to exist in specific ratios. For a CHB leg with $M$ cells, the proposed technique identifies and isolates open-circuit switch faults in less than $2M$ measurement (sampling) cycles, and verification is completed in less than one full fundamental cycle. Experimentally obtained data demonstrate the efficacy of the proposed fault detection and isolation technique.

Reliable Modular Multilevel Converter Fault Detection With Redundant Voltage Sensor

Abstract: This paper presents a fault-tolerant configuration for the modular multilevel converter (MMC). The procedure is able to detect faults in voltage sensors and semiconductor switching devices, and it can reconfigure the system so that it can keep on operating. Both switch and sensor faults can be detected by comparing the output voltage of a set of submodules (SMs), which is measured by a so-called supervisory sensor, with two calculated reference voltages. Faults in the supervisory sensors are also considered. Sensor faults are overcome by using a measuring technique based on estimates that are periodically updated with the voltage measurements of the supervisory sensors. Additional SMs are included in the arms so that the MMC can bypass a faulty SM and continue operating without affecting the output voltage of the phase-leg. Experimental results obtained from a low-power MMC prototype are presented in order to demonstrate the effectiveness of the proposed techniques.

Fault-Tolerant Operation of Three-Phase Cascaded H-Bridge Converters Using an Auxiliary Module

Abstract: This paper presents a new fault-tolerant solution for cascaded H-bridge (CHB) converters, which generates equal output voltages in both prefault and single fault conditions. To generate a balanced three-phase voltage with the highest amplitude regardless of the fault location and its type, an auxiliary module is employed in series with the CHB converter. The auxiliary module is a two-level voltage-source inverter with a capacitive dc link. The module is brought to the circuit after fault detection and its capacitor is charged by a novel algorithm to the reference value. Then, using the space vector modulation, the inverter's reference space vector is synthesized and the voltage of auxiliary module is kept constant. The validation of the proposed method is confirmed by simulations and experiments on a three-phase five-level CHB converter.

A New Sensor Fault Isolation Method for T–S Fuzzy Systems

Abstract: This paper is concerned with the fault isolation problem for T–S fuzzy systems with sensor faults. With the help of a set theoretic description of T–S fuzzy models, a new fault isolation scheme is proposed. It consists of a set of fuzzy observers and each of them corresponds to a specified sensor, where the antecedent and consequent parts of the observer are independent on the sensor output. Different from the existing approaches, the premise variables, which do not depend on the specified sensor output but depend on the other sensor outputs, are used in the proposed observer, which has the potential to lead to a better fault isolation performance. In the end, an example is given to show the effectiveness of the fault isolation method.

A Single-Phase Five-Level Inverter Topology With Switch Fault-Tolerance Capabilities

Abstract: Low reliability is one of the major concerns of multilevel inverter due to the requirement of a large number of semiconductor devices as compared to two-level inverters. Thus, in this paper, a novel topology of multilevel inverter is proposed that can tolerate both open and short-circuit faults on its switches. The proposed topology also reduces dc sources and capacitors as compared to the conventional and recently proposed fault-tolerant topologies. Two types of solutions are provided in order to make the proposed topology fault tolerant; first provides a partial solution to fault, while the second provides a complete solution to fault. In addition, a novel switching strategy is proposed to reduce the amount of capacitor voltage ripples under normal and postfault conditions. Also, the proposed switching scheme offers an additional advantage of self-voltage balancing of its capacitor voltage both under normal as well as postfault conditions. To validate the proposed concepts, simulation and experimental analysis are carried out and different results are presented to show the viability of the proposed topology under normal operation, during fault and postfault conditions.

Capturing High-Discriminative Fault Features for Electronics-Rich Analog System via Deep Learning

Abstract: Fault detection and isolation (FDI) is very difficult for electronics-rich analog systems due to its sophisticated mechanism and variable operational conditions. Traditionally, FDI in such systems is done through the monitoring of deviation of output signals in voltage or current at system level, which commonly arises from the degradation of one or more critical components. Therefore, FDI can be transformed to a multiclass classification task given the extracted features of the output signals in voltage or current of the circuit. Traditional feature extraction on the circuit output is mostly based on time-domain, frequency-domain, or time-frequency signal processing, which collapse high-dimensional raw signals into a lower dimensional feature set. Such low-dimensional feature set usually suffers from information loss so as to affect the accuracy of the later fault diagnosis. In order to retain as much information as possible, deep learning is proposed which employs a hierarchical structure to capture the different levels of semantic representations of the signals. In this paper, a novel fault diagnostic application of Gaussian–Bernoulli deep belief network (GB-DBN) for electronics-rich analog systems is developed which can more effectively capture the high-order semantic features within the raw output signals. The novel fault diagnosis is validated experimentally on two typical analog filter circuits. Experimental results show the fault diagnosis based on GB-DBN is with superior diagnostic performance than the traditional feature extraction methods.

Fault-Tolerant Control for an Internet-Based Three-Tank System: Accommodation to Sensor Bias Faults

Abstract: This paper focuses on the fault-tolerant control problem for an Internet-based three-tank system in the presence of possible sensor bias faults. The Internet-based three-tank system is an experimental setup that can be regarded as a typical networked system for evaluating networked fault-diagnosis and fault-tolerant control methods. Packet dropout phenomenon in the sensor-to-controller link is considered in this paper, and the fault type we deal with is chosen as the sensor bias fault. Fault-diagnosis unit is designed toward an auxiliary system. Sensor bias faults can be detected by comparing the residual signal generated by the fault detection filter and a prescribed threshold. After that, the fault can be isolated by using the residual analysis approach. Once the fault is isolated, it can be estimated iteratively in the least-squares sense. A fault accommodation method is proposed, and a fault-tolerant control strategy is achieved based on the fault information provided by the fault-diagnosis unit. The approach brought forward in this paper is demonstrated via an experimental study on the practical Internet-based three-tank system. Results show the effectiveness and the applicability of the proposed techniques.

Open-Phase Fault Detection of a Five-Phase Permanent Magnet Assisted Synchronous Reluctance Motor Based on Symmetrical Components Theory

Abstract: This paper presents a novel approach for open-phase fault detection of a five-phase permanent magnet assisted synchronous reluctance motor (PMa-SynRM). Under faults, the five-phase PMa-SynRM is expected to run at fault-tolerant control (FTC) mode, otherwise it draws a large amount of current with a significant reduction in the reluctance torque. To successfully achieve FTC operation of five-phase PMa-SynRM, the accurate detection of a fault condition has to be preceded. With the best of these authors knowledge, the detection of faults has been limitedly studied for five-phase motors. The analysis of open-phase fault in five-phase machine involves complicated conditions including single-phase open fault, two-phase adjacent fault, and two-phase nonadjacent fault. To perform the timely fault-tolerant operation, those faults have to be accurately analyzed and detected. In this paper, a novel symmetrical components (SCs) analysis is utilized to extract the feature of those fault conditions. This analysis will provide the types of faults by logically analyzing the pattern of magnitude and phase angle changes of the fundamental signal in the SCs. The proposed method has been comprehensively analyzed through theoretical derivation, finite-element simulations, and experimental testing through a 5 hp PMa-SynRM controlled by TI-DSP F28335.

A Novel Fault Location Method and Algorithm for DC Distribution Protection

Abstract: DC fault current is contributed by various distributed energy resources in dc distribution systems. The tightly coupled dc distribution systems have relatively low line impedance values. The fault current increases fast because of the low impedance. Some converters in dc distribution systems include fault current limiting function. The controlled fault currents at different locations are very close. Thus, it is important to design a reliable and fast fault detection and location method for dc distribution systems. This paper proposes a novel local measurement-based fault location algorithm for tightly coupled dc distribution systems. The proposed fault location algorithm can estimate the equivalent inductance between a protective device and a fault in less than 1 ms. The performance of the developed protection algorithm was validated by numerical simulation and hardware tests.

An Alternative Method for Locating Faults in Transmission Line Networks Based on Time Reversal

Abstract: We present an alternative method based on the time-reversal process to locate faults in transmission line networks. The proposed procedure considers different media for the forward and the backward propagation phases. Specifically, the transverse branch representing the fault is removed from the circuit in the backward propagation since its location represents the solution of the process and, therefore, is not known in advance. The advantage of the proposed method is twofold. First, the proposed backward model requires only one simulation for the time-reversed backward propagation phase, thus reducing significantly the computational burden. Second, we demonstrate that this modified backward propagation medium satisfies a property such that the fault location can be identified by computing, in the frequency domain, the argument of the voltage along the line. The theory is first formulated for the case of a lossless homogeneous single-phase transmission line; then, its applicability is extended to lossy inhomogeneous transmission line networks. A single-phase inhomogeneous transmission line and an inhomogeneous Y-shape network are specifically considered to support this claim. We show that the proposed procedure can provide high fault location accuracy (i.e., in the range of $\pm 1$ m), using only one observation point. Furthermore, we propose a criterion to link the bandwidth of the sampling system to the desired fault location accuracy.

Multi-Agent-Based Technique for Fault Location, Isolation, and Service Restoration

Abstract: This paper proposes a communication-assisted fault localization, isolation, and restoration method for microgrids based on a multi-agent system. The proposed system comprises distributed agents, located in the middle and at the two ends of a protection section, which will detect a fault through phase angle comparison of current signals at both sides of a given distribution line. The agents then send trips signal to corresponding circuit breakers accordingly. The importance of the proposed protection technique is twofold. First, it eliminates the use of voltage transformers and thus reduces costs. Second, it does not require transfer of data along long distances, which decreases the delay time for fault isolation. Power restoration processes following the fault clearance considering voltage, frequency, and power flow constraints in the microgrid under study were also performed. Simulation of the proposed protection methodology was presented followed by experimental verification. The experimental results showed excellent agreement with the simulated protection scheme.

Fault diagnosis and compensation for two‐dimensional discrete time systems with sensor faults and time‐varying delays

Abstract: Summary A fault diagnosis and compensation problem for two-dimensional discrete time systems with time-varying state delays is studied in this paper. The concerned two-dimensional systems are described by the Fornasisi–Marchesini second model and are subject to unknown disturbances. First, a fault detection and diagnosis module is designed to obtain the information on sensor faults; a new fault detection and diagnosis integrated design, using the observer based on descriptor system approach, is proposed to detect and estimate the sensor faults. The integrated design can maximize the fault detection rate for a given false alarm rate. Sufficient conditions for the existence of the integrated fault detection and diagnosis design are derived in the context of norm evaluation and provided in terms of matrix inequalities. Second, a fault-tolerant control module is proposed upon an existing output feedback controller. When the sensor fault occurs, the faulty measurement can be identified and corrected by the proposed fault detection and diagnosis module. In this case, the feedback controller can guarantee the performance of the closed-loop system even when encountering sensor faults. Finally, the proposed method is applied to a thermal process to illustrate its effectiveness. Copyright © 2017 John Wiley & Sons, Ltd.

The Detection of Parallel Arc Fault in Photovoltaic Systems Based on a Mixed Criterion

Abstract: This paper presents the problem of parallel arc fault in the DC side of a photovoltaic (PV) system. First, an experimental platform for arc fault of a PV system was built to simulate the DC side parallel arc fault in the PV system. By extracting the current waveform at the exit of PV panels, a study was conducted on the characteristics of the current during parallel arc fault. The result of Fourier transform on the current suggests that the high-frequency component of the current mainly concentrates in the frequency range of 126–250 kHz. According to the analysis of arc fault characteristic in time domain and frequency domain, reversal current maximum, modulus maximum, and energy are chosen as the criteria to detect parallel arc fault. And finally, a mixed criterion is proposed to overcome the shortcomings of the above-mentioned criteria and its reliability is verified by experiments. It provides a theoretical basis for the detection of the parallel arc fault in the PV system.

Investigation of a Fault-tolerant Three-level T-type Inverter System

Abstract: In recent years, a three-level T-type inverter has attracted considerable attention due to its advantages, such as simple structure and higher efficiency. However, the reliability of a three-level T-type inverter is particularly important as increased power switches are used. Therefore, a fault-tolerant three-level T-type inverter is proposed in this paper. Fault diagnosis and fault-tolerant control strategies for power switches both in half-bridge and neutral-point bridge are investigated. Under fault-tolerant operation, redundant fourth-leg is used to balance neutral-point voltage and the low-frequency voltage oscillation is eliminated completely. The fault ride-through capability and high-quality output waveforms can be obtained. The effectiveness of the proposed inverter topology and control methods is validated by the simulation and experimental results.

Logic-Based Methods for Intelligent Fault Diagnosis and Recovery in Power Electronics

Abstract: This paper presents generalized logic-based methods for intelligent fault diagnosis in power electronic converters based on correlation between faults and basic measurements. Fault recovery is then applied based on this correlation by using necessary signals and quantities from existing measurements. The main purpose of the proposed fault diagnosis methods is for power electronic systems to survive from fault conditions that could occur in various components, to cope with the notion of a smart grid and extend their lifetime. The proposed methods are online, i.e., real-time or near-real-time, and can be applied to any power electronic system. Existing intelligent control of power electronic systems along with various short- and open-circuit faults in major power electronic components are reviewed. Two methods are established to diagnose faults and engage redundancy for fault recovery with one method using combinational logic and another using fuzzy logic. In both methods, two quantities are observed for each of the measured signals: 1) the signal's average value and 2) the signal's RMS value. Total harmonic distortion is also used in some simulations but is not required in experimental implementation. A systematic methodology to reduce the number of measured quantities while maintaining effective diagnosis is introduced. A solar PV microinverter in standalone mode is used as an example testing platform for the proposed methods. A simulation model is experimentally validated and the effect of each fault on different voltage and current measurements are observed, then both methods are tested in simulation and hardware. Results show the ability of both methods to diagnose several faults in the inverter's power stage along with their ability to engage redundancy for fault recovery.

Signal Model-Based Fault Coding for Diagnostics and Prognostics of Analog Electronic Circuits

Abstract: Analog circuits have been extensively used in industrial systems, and their failure may make the systems work abnormally and even cause accidents. In order to monitor their status, detect faults, and predict their failure early, this study proposes a signal model-based fault coding to monitor the circuit response after being stimulated to perform a fault diagnosis without training a large amount of sample data and fault classifiers. Manifold features extracted from circuit responses are associated with a fault-indicating curve in the feature space, in which a group of fault bases are uniformly and continuously distributed along with gradual deviation from the nominal value of one critical component. These bases can be deployed in a factory setting but used during field operation. Fault coding is converted to a novel optimization problem, and the optimized solution forms a fault code representing fault class, suitable for realizing fault detection, and isolation for different components. A fault indicator based on comparison between fault codes can describe performance degradation trends. To improve the prediction accuracy, historical degradation data are collected and considered as a priori exemplars, and a novel exemplar-based conditional particle filter is proposed to track a degradation process for the prediction of remaining useful performance. Case studies on two analog filter circuits demonstrate that the proposed method achieves relatively high fault diagnosis and prognosis accuracy. The main advantages of our study are two-fold: first, the high diagnostic accuracy can still be obtained even if there is no large amount of training data; second, the prognostic effect remains relatively stable whenever triggering prognosis module.