Showing papers on "Fault coverage published in 2014"

Impedance-Based Fault Location in Transmission Networks: Theory and Application

Abstract: A number of impedance-based fault location algorithms have been developed for estimating the distance to faults in a transmission network. Each algorithm has specific input data requirements and makes certain assumptions that may or may not hold true in a particular fault location scenario. Without a detailed understanding of the principle of each fault-locating method, choosing the most suitable fault location algorithm can be a challenging task. This paper, therefore, presents the theory of one-ended (simple reactance, Takagi, modified Takagi, Eriksson, and Novosel et al. ) and two-ended (synchronized, unsynchronized, and current-only) impedance-based fault location algorithms and demonstrates their application in locating real-world faults. The theory details the formulation and input data requirement of each fault-locating algorithm and evaluates the sensitivity of each to the following error sources: 1) load; 2) remote infeed; 3) fault resistance; 4) mutual coupling; 5) inaccurate line impedances; 6) DC offset and CT saturation; 7) three-terminal lines; and 8) tapped radial lines. From the theoretical analysis and field data testing, the following criteria are recommended for choosing the most suitable fault-locating algorithm: 1) data availability and 2) fault location application scenario. Another objective of this paper is to assess what additional information can be gleaned from waveforms recorded by intelligent electronic devices (IEDs) during a fault. Actual fault event data captured in utility networks is exploited to gain valuable feedback about the transmission network upstream from the IED device, and estimate the value of fault resistance.

Test case purification for improving fault localization

Abstract: Finding and fixing bugs are time-consuming activities in software development. Spectrum-based fault localization aims to identify the faulty position in source code based on the execution trace of test cases. Failing test cases and their assertions form test oracles for the failing behavior of the system under analysis. In this paper, we propose a novel concept of spectrum driven test case purification for improving fault localization. The goal of test case purification is to separate existing test cases into small fractions (called purified test cases) and to enhance the test oracles to further localize faults. Combining with an original fault localization technique (e.g., Tarantula), test case purification results in better ranking the program statements. Our experiments on 1800 faults in six open-source Java programs show that test case purification can effectively improve existing fault localization techniques.

Automatic and Fast Faulted Line-Section Location Method for Distribution Systems Based on Fault Indicators

Abstract: Fault indicating devices such as fault indicators (FIs) have been widely used in distribution systems to improve reliability and reduce outage duration. Recently, FIs with communication interfaces are integrated into distribution automation (DA) to further reduce fault-finding time by reporting FIs' statuses back to control center. When faults occur, a lot of alarms and fault information are received from Outage Management System (OMS), Trouble Call System (TCS) and Customer Information System (CIS) and are shown to system operators. As a result, the identification of faulted line sections in a wide-ranging distribution system from FIs' statuses is not an easy task, especially when multiple faults occur simultaneously and/or distributed generators (DGs) are connected. An automatic and fast faulted line-section location method for distribution systems based on FIs is proposed in this paper. The line sections between adjacent FIs can be treated as a possible fault location (PFL) and the fault current detected by the FI can be considered as line current (LC) flowing between the adjacent PFLs. A relationship matrix between PFLs and LCs is then derived and used to design the proposed automatic and fast faulted line-section location method. The faulted line sections can then be located effectively and efficiently by the proposed method. Test results for an actual distribution system demonstrate the validity of the proposed faulted line-section location method.

Differential Fault Intensity Analysis

Abstract: Recent research has demonstrated that there is no sharp distinction between passive attacks based on side-channel leakage and active attacks based on fault injection. Fault behavior can be processed as side-channel information, offering all the benefits of Differential Power Analysis including noise averaging and hypothesis testing by correlation. This paper introduces Differential Fault Intensity Analysis, which combines the principles of Differential Power Analysis and fault injection. We observe that most faults are biased - such as single-bit, two-bit, or three-bit errors in a byte - and that this property can reveal the secret key through a hypothesis test. Unlike Differential Fault Analysis, we do not require precise analysis of the fault propagation. Unlike Fault Sensitivity Analysis, we do not require a fault sensitivity profile for the device under attack. We demonstrate our method on an FPGA implementation of AES with a fault injection model. We find that with an average of 7 fault injections, we can reconstruct a full 128-bit AES key.

A survey on simulation-based fault injection tools for complex systems

Abstract: Dependability is a key decision factor in today's global business environment. A powerful method that permits to evaluate the dependability of a system is the fault injection. The principle of this approach is to insert faults into the system and to monitor its responses in order to observe its behavior in the presence of faults. Several fault injection techniques and tools have been developed and experimentally tested. They could be mainly grouped into three categories: hardware fault injection, simulation-based fault injection, and emulation-based fault injection. This paper presents a survey on the simulation-based fault injection techniques, with a focus on complex micro-processor based systems.

Transmission-Line Fault Analysis Using Synchronized Sampling

Abstract: An automated analysis approach, which can automatically characterize fault and subsequent relay operation, is the focus of this paper. It utilizes synchronized samples captured during transients from both ends of the transmission line to detect, classify, and locate transmission-line faults and can verify that the tripped line has indeed experienced a fault. The proposed method is tested for several faults simulated on an IEEE 118-bus test system and it has been concluded that it can detect and classify a fault using prefault and postfault recorded samples within 7 ms of fault inception and can accurately locate a fault with 3% accuracy. This time response performance is highly desirable since with the increasing use of modern circuit breakers, which can open the faulty line in less than two cycles, the time window of the captured waveforms is significantly reduced due to the unavailability of measurement signals after breakers open.

An overview of transmission line protection by artificial neural network: fault detection, fault classification, fault location, and fault direction discrimination

Abstract: Contemporary power systems are associated with serious issues of faults on high voltage transmission lines. Instant isolation of fault is necessary to maintain the system stability. Protective relay utilizes current and voltage signals to detect, classify, and locate the fault in transmission line. A trip signal will be sent by the relay to a circuit breaker with the purpose of disconnecting the faulted line from the rest of the system in case of a disturbance for maintaining the stability of the remaining healthy system. This paper focuses on the studies of fault detection, fault classification, fault location, fault phase selection, and fault direction discrimination by using artificial neural networks approach. Artificial neural networks are valuable for power system applications as they can be trained with offline data. Efforts have been made in this study to incorporate and review approximately all important techniques and philosophies of transmission line protection reported in the literature till June 2014. This comprehensive and exhaustive survey will reduce the difficulty of new researchers to evaluate different ANN based techniques with a set of references of all concerned contributions.

A Comparison of Test Case Prioritization Criteria for Software Product Lines

Abstract: Software Product Line (SPL) testing is challenging due to the potentially huge number of derivable products. To alleviate this problem, numerous contributions have been proposed to reduce the number of products to be tested while still having a good coverage. However, not much attention has been paid to the order in which the products are tested. Test case prioritization techniques reorder test cases to meet a certain performance goal. For instance, testers may wish to order their test cases in order to detect faults as soon as possible, which would translate in faster feedback and earlier fault correction. In this paper, we explore the applicability of test case prioritization techniques to SPL testing. We propose five different prioritization criteria based on common metrics of feature models and we compare their effectiveness in increasing the rate of early fault detection, i.e. a measure of how quickly faults are detected. The results show that different orderings of the same SPL suite may lead to significant differences in the rate of early fault detection. They also show that our approach may contribute to accelerate the detection of faults of SPL test suites based on combinatorial testing.

At-Speed Distributed Functional Testing to Detect Logic and Delay Faults in NoCs

Abstract: In this work, we propose a distributed functional test mechanism for NoCs which scales to large-scale networks with general topologies and routing algorithms. Each router and its links are tested using neighbors in different phases. The router under test is in test mode while all other parts of the NoC are operational. We use triple module redundancy (TMR) for the robustness of all testing components that are added into the switch. Experimental results show that our functional test approach can detect stuck-at, short and delay faults in the routers and links. Our approach achieves 100 percent stuck-at fault coverage for the data path and 85 percent for the control paths including routing logic, FIFO's control path, and the arbiter of a $(5 \times 5)$ router. We also show that our approach is able to detect delay faults in critical control and data paths. Synthesis results show that the area overhead of our test components with TMR support is 20 percent for covering stuck-at, delay, and short-wire faults and 7 percent for covering only stuck-at and delay faults in the $(5 \times 5)$ router. Simulation results show that our online testing approach has an average latency overhead of 3 percent in PARSEC traffic benchmarks on an $(8 \times 8)$ NoC.

Harnessing Soft Computations for Low-Budget Fault Tolerance

Abstract: A growing number of applications from various domains such as multimedia, machine learning and computer vision are inherently fault tolerant. However, for these soft workloads, not all computations are fault tolerant (e.g., a loop trip count). In this paper, we propose a compiler-based approach that takes advantage of soft computations inherent in the aforementioned class of workloads to bring down the cost of software-only transient fault detection. The technique works by identifying a small subset of critical variables that are necessary for correct macro-operation of the program. Traditional duplication and comparison are used to protect these variables. For the remaining variables and temporaries that only affect the micro-operation of the program, strategic expected value checks are inserted into the code. Intuitively, a computation-chain result near the expected value is either correct or close enough to the correct result so that it does not matter for non-critical variables. Overall, the proposed solution has, on average, only 19.5% performance overhead and reduces the number of silent data corruptions from 15% down to 7.3% and user-visible silent data corruptions from 3.4% down to 1.2% in comparison to an unmodified application. This unacceptable silent data corruption rate is even lower than a traditional full duplication scheme that has, on average, 57% overhead.

Combination of Model-based Observer and Support Vector Machines for Fault Detection of Wind Turbines

Abstract: Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontal-axis wind turbine composed of three blades and a full converter. The support vector approach is data-based and is therefore robust to process knowledge. It is based on structural risk minimization which enhances generalization even with small training data set and it allows for process nonlinearity by using flexible kernels. In this work, a radial basis function is used as the kernel. Different parts of the process are investigated including actuators and sensors faults. With duplicated sensors, sensor faults in blade pitch positions, generator and rotor speeds can be detected. Faults of type stuck measurements can be detected in 2 sampling periods. The detection time of offset/scaled measurements depends on the severity of the fault and on the process dynamics when the fault occurs. The converter torque actuator fault can be detected within 2 sampling periods. Faults in the actuators of the pitch systems represents a higher difficulty for fault detection which is due to the fact that such faults only affect the transitory state (which is very fast) but not the final stationary state. Therefore, two methods are considered and compared for fault detection and isolation of this fault: support vector machines and a Kalman-like observer. Advantages and disadvantages of each method are discussed. On one hand, support vector machines training of transitory states would require a big amount of data in different situations, but the fault detection and isolation results are robust to variations in the input/operating point. On the other hand, the observer is model-based, and therefore does not require training, and it allows identification of the fault level, which is interesting for fault reconfiguration. But the observability of the system is ensured under specific conditions, related to the dynamics of the inputs and outputs. The whole fault detection and isolation scheme is evaluated using a wind turbine benchmark with a real sequence of wind speed.

Adaptive Fault Diagnosis Algorithm for Controller Area Network

Abstract: A controller area network (CAN)-based distributed system may develop faults at run-time. These faults need to be detected and diagnosed. This paper proposes a new algorithm named adaptive fault diagnosis algorithm for CAN (AFDCAN). It is designed for low-cost resource-constrained distributed embedded systems. The proposed algorithm detects all faulty nodes on the CAN. It allows new node entry and reentry of repaired faulty nodes during a diagnostic cycle. AFDCAN is found to provide high fault tolerance and to ensure reliable communication. It uses single-channel communication deploying the bus-based standard CAN protocol. A hardware implementation of the proposed algorithm has been used to obtain the results. The results show that the proposed algorithm diagnoses all faults in the system. Analysis of the proposed algorithm proves that the algorithm uses a definite and bounded number of testing rounds and messages to complete one diagnostic cycle.

Accuracy improvement of impedance-based fault location method for power distribution network using distributed-parameter line model

Abstract: SUMMARY Power Distribution System (PDS) is spread on different places. Therefore, PDS has many laterals and load taps. Accurate fault locating in PDS causes to improve reliability indices and its efficiency. In this paper, an improved method is suggested for fault location in PDS, which has a high accuracy. In the proposed algorithm, by using phase domain of distributed-parameter line model, a fifth-order algebraic equation of fault distance is obtained, which can improve the accuracy of determined fault distance for all types of faults. The proposed method is tested under different fault resistances in which the results show low sensitivity to this parameter. To evaluate the accuracy of the proposed method, the modified IEEE 34 Node Test Feeder is used, and its efficiency and accuracy is proved. Copyright © 2012 John Wiley & Sons, Ltd.

Fault Detection and Diagnosis

Abstract: The article describes the detection and isolation (diagnosis) of faults (major equipment and sensor/actuator malfunctions) in engineering systems. The simpler, and less powerful methods do not rely on any mathematical model of the system; these include limit checking, special and multiple sensors, frequency analysis, and fault-tree analysis. More advanced methods use mathematical models obtained from first principles or from experimental data. Such methods include on-line parameter estimation, consistency checking, and principal component analysis. Two examples, one related to a simple electrical circuit and the other to a car-engine subsystem, demonstrate the use of some of the methods. Keywords: fault detection; fault diagnosis; limit checking; frequency analysis; fault trees; parameter estimation; consistency relations; principal component analysis

Fault location on branched networks using a multiended approach

Abstract: Existing travelling-wave fault-location methods are known to perform poorly on branched networks. In this paper, a new fault-location method for use on highly branched networks is demonstrated. The method is based on the retrieval of global positioning system time-stamped arrival times of the fault-induced high-frequency transient. It is shown that for an unambiguous estimation of the fault location, a time-stamp is required from a fault recorder located in each of the two directions from the fault position; therefore, complete coverage of a network can be achieved with a fault recorder on each branch termination. The system is also capable of locating the origin of incipient faults and, therefore, provides a means of detecting potential future faults so remedial action can be taken.

Software-Based Control Flow Checking Against Transient Faults in Industrial Environments

Abstract: Mechatronic systems operating in industrial environments are subject to a variety of threats because of harsh conditions. Industrial systems usually use commercial off-the shelf (COTS) equipment which are not robust and safe against hostile conditions and therefore require fault-tolerance considerations. This paper presents a novel and efficient method for online detection of control flow errors, called software-based control flow checking (SCFC). It is implemented purely in software and does not manipulate the hardware architecture of the system. Redundant instructions and signatures are embedded into the program at compile time and are utilized for control flow checking at run time. The signatures of the basic blocks are derived from the program graph. It is shown in the paper that SCFC method can increase single detection capability to 14.7% and the fault coverage to 6.12% averagely in comparison with other methods without any increase in memory and performance overheads. In the paper, besides experimental evaluations, analytical evaluations are also carried out, based on probability principles. The detection ability of each method used is thus computed. These computations verify the experimental results and show that SCFC can detect more errors than other methods suggested in literature. Considering the memory limitations in some (such as space) applications and the trend towards the requirement for faster execution of programs, we suggest a novel metric called fitness parameter which incorporates these. It is a better measure than the previously proposed ones since it considers the fault coverage, the memory overhead and the execution time (performance overhead) of each method simultaneously, as well as the detection capability.

Data-driven fault diagnosis for an automobile suspension system by using a clustering based method

Abstract: This paper concerns the issues of fault diagnosis and monitoring for an automobile suspension system where only accelerator sensors in the four corners of the car body are available. A clustering based method is proposed to detect the fault happened in the spring, and the Fisher discriminant analysis is applied to isolate the root factor for the fault. Different from most of the existing approaches, the pure data-driven characteristic enables this method to serve as an on-line fault diagnosis and monitoring tool without suspension model or fault features known as a prior. Moreover, this method can classify different reductions in the spring coefficient into one fault rather than different faults. The effectiveness of the proposed method is finally illustrated on an automobile suspension benchmark.

An nhpp software reliability growth model with imperfect debugging and error generation

Abstract: In this paper, we propose a non-homogeneous Poisson process (NHPP) based software reliability growth model (SRGM) in the presence of modified imperfect debugging and fault generation phenomenon. The testing team may not be able to remove a fault perfectly on observation of a failure due to the complexity of software systems and incomplete understanding of software, and the original fault may remain, or get replaced by another fault causing error generation. We have proposed an exponentially increasing fault content function and constant fault detection rate. The total fault content of the software for our proposed model increases rapidly at the beginning of the testing process. It grows gradually at the end of the testing process because of increasing efficiency of the testing team with testing time. We use the maximum likelihood estimation method to estimate the unknown parameters of the proposed model. The applicability of our proposed model and comparisons with established models in terms of goodness of fit and predictive validity have been presented using five known software failure data sets. Experimental results show that the proposed model gives a better fit to the real failure data sets and predicts the future behavior of software development more accurately than the traditional SRGMs.

Systems and methods for event detection and diagnosis

Abstract: Detection of event conditions in an industrial plant includes receiving process data corresponding to one or more sensors, estimating normal statistics from the process data, estimating abnormal statistics from the process data with potential!)' abnormal operation of the one or more components, determining a fault model from the estimated normal and abnormal statistics, the fault model including a learning matrix, one or more fault indices indicating a likelihood of an occurrence of one or more fault events, and a fault threshold corresponding to the one or more sensors, determining one or more further fault indices from the further process data: applying the fault threshold to the one or more further fault indices, and indicating a further occurrence of the one or more fault events when a magnitude of the one or more further fault indices exceeds the fault threshold corresponding to the one or more sensors.

Apparatus and method for selection of fault detection algorithms for a building management system

Abstract: An apparatus for detecting component faults monitored by a building management system (BMS), including an automatic fault detection (AFD) element, coupled to the BMS, that monitors samples generated by the BMS indicating operative states of the components, and that employs the data samples to determine if one or more of the components are faulty. The AFD element includes a run time modeling element and a fault detection algorithm element. The modeling element employs the samples as inputs to execute one or more fault algorithms retrieved from a system model, and generates outputs that indicate if the one or more of the components are faulty. The algorithm element is coupled to the system model, and employs normalized/standardized datapoints representing the samples to select the one or more fault algorithms for storage in the system model, where the one or more fault algorithms are selected from a standard fault algorithm data base.

Assessing neural networks for sensor fault detection

Abstract: The idea of “smart sensing” includes a permanent monitoring and evaluation of sensor data related to possible measurement faults This concept requires a fault detection chain covering all relevant fault types of a specific sensor Additionally, the fault detection components have to provide a high precision in order to generate a reliable quality indicator Due to the large spectrum of sensor faults and their specific characteristics these goals are difficult to meet and error prone The developer manually determines the specific sensor characteristics, indicates a set of detection methods, adjusts parameters and evaluates the composition In this paper we exploit neural-network approaches in order to provide a general solution covering typical sensor faults and to replace complex sets of individual detection methods For this purpose, we identify an appropriate set of fault relevant features in a first step Secondly, we determine a generic neural-network structure and learning strategy adaptable for detecting multiple fault types Afterwards the approach is applied on a common used sensor system and evaluated with deterministic fault injections

Internal Leakage Fault Detection and Tolerant Control of Single-Rod Hydraulic Actuators

Abstract: The integration of internal leakage fault detection and tolerant control for single-rod hydraulic actuators is present in this paper. Fault detection is a potential technique to provide efficient condition monitoring and/or preventive maintenance, and fault tolerant control is a critical method to improve the safety and reliability of hydraulic servo systems. Based on quadratic Lyapunov functions, a performance-oriented fault detection method is proposed, which has a simple structure and is prone to implement in practice. The main feature is that, when a prescribed performance index is satisfied (even a slight fault has occurred), there is no fault alarmed; otherwise (i.e., a severe fault has occurred), the fault is detected and then a fault tolerant controller is activated. The proposed tolerant controller, which is based on the parameter adaptive methodology, is also prone to realize, and the learning mechanism is simple since only the internal leakage is considered in parameter adaptation and thus the persistent exciting (PE) condition is easily satisfied. After the activation of the fault tolerant controller, the control performance is gradually recovered. Simulation results on a hydraulic servo system with both abrupt and incipient internal leakage fault demonstrate the effectiveness of the proposed fault detection and tolerant control method.