Showing papers on "Fault coverage published in 2017"
20 May 2017
TL;DR: A design space is identified that includes many previously-studied fault localization techniques as well as hundreds of new techniques, and which factors in the design space are most important, using an overall set of 395 real faults.
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
338 citations
TL;DR: Both the simulation and experimental results show that the optimized KELM based fault diagnosis model can achieve high accuracy, reliability, and good generalization performance.
228 citations
TL;DR: In this article, the authors provide 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 sixphase induction machines (asymmetrical, symmetrical, and dual three phase).
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.
193 citations
TL;DR: 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, the working principles, advantages and disadvantages of past works related to each fault location technique are highlighted in this paper.
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.
188 citations
TL;DR: This paper deals with the problem of fault detection and diagnosis in sensors considering erratic, drift, hard-over, spike, and stuck faults, and shows that an increase in the number of features hardly increases the total accuracy of the classifier, but using ten features gives the highest accuracy for fault classification in an SVM.
Abstract: This paper deals with the problem of fault detection and diagnosis in sensors considering erratic, drift, hard-over, spike, and stuck faults. The data set containing samples of the abovementioned fault signals was acquired as follows: normal data signals were obtained from a temperature-to-voltage converter by using an Arduino Uno microcontroller board and MATLAB. Then, faults were simulated in normal data to get 100 samples of each fault, in which one sample is composed of 1000 data elements. A support vector machine (SVM) was used for data classification in a one-versus-rest manner. The statistical time-domain features, extracted from a sample, were used as a single observation for training and testing SVM. The number of features varied from 5 to 10 to examine the effect on accuracy of SVM. Three different kernel functions used to train SVM include linear, polynomial, and radial-basis function kernels. The fault occurrence event in fault samples was chosen randomly in some cases to replicate a practical scenario in industrial systems. The results show that an increase in the number of features from 5 to 10 hardly increases the total accuracy of the classifier. However, using ten features gives the highest accuracy for fault classification in an SVM. An increase in the number of training samples from 40 to 60 caused an overfitting problem. The $k$ -fold cross-validation technique was adopted to overcome this issue. The increase in number of data elements per sample to 2500 increases the efficiency of the classifier. However, an increase in the number of training samples to 400 reduces the capability of SVM to classify stuck fault. The receiver operating characteristics curve comparison shows the efficiency of SVM over a neural network.
182 citations
TL;DR: An online sequential prediction method for imbalanced fault diagnosis problem is proposed based on extreme learning machine and proves that, even existing information loss, the proposed method has lower bound of the model reliability.
177 citations
TL;DR: This paper provides a comprehensive review of popular fault detection techniques, addressing all major types of faults in PV systems, and proposes a new fault detection technique to identify the type and location (module level) of a fault.
175 citations
TL;DR: In this article, a model-based fault detection and identification (FDI) method for switching power converters using a modelbased state estimator approach is presented. But 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 switches.
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.
167 citations
TL;DR: Wang et al. as discussed by the authors proposed two representative smoothing techniques, which are based on a generic fault detection index in multivariate statistical process monitoring (MSPM), to detect incipient faults.
124 citations
TL;DR: In this article, the authors proposed a comprehensive multi-terminal HVDC protection scheme based on artificial neural network (ANN) and high frequency components detected from fault current signals only.
106 citations
TL;DR: This paper considers the problem of achieving a given reliability target for a set of periodic real-time tasks running on a multicore system with minimum energy consumption, and proposes dynamic adaptation schemes to reduce the concurrent execution of the replicas of a given task and to take advantage of early completions.
Abstract: On emerging multicore systems, task replication is a powerful way to achieve high reliability targets. In this paper, we consider the problem of achieving a given reliability target for a set of periodic real-time tasks running on a multicore system with minimum energy consumption. Our framework explicitly takes into account the coverage factor of the fault detection techniques and the negative impact of Dynamic Voltage Scaling (DVS) on the rate of transient faults leading to soft errors. We characterize the subtle interplay between the processing frequency, replication level, reliability, fault coverage, and energy consumption on DVS-enabled multicore systems. We first develop static solutions and then propose dynamic adaptation schemes in order to reduce the concurrent execution of the replicas of a given task and to take advantage of early completions. Our simulation results indicate that through our algorithms, a very broad spectrum of reliability targets can be achieved with minimum energy consumption thanks to the judicious task replication and frequency assignment.
TL;DR: This brief presents the systematic design and real-time experimental results of a fault detection, isolation, and accommodation algorithm for quadrotor actuator faults using nonlinear adaptive estimation techniques.
Abstract: This brief presents the systematic design and real-time experimental results of a fault detection, isolation, and accommodation algorithm for quadrotor actuator faults using nonlinear adaptive estimation techniques. The fault diagnosis architecture consists of a nonlinear fault detection estimator and a bank of nonlinear adaptive fault isolation estimators designed based on the functional structures of the faults under consideration. Adaptive thresholds for fault detection and isolation are systematically designed to enhance the robustness and fault sensitivity of the diagnostic algorithm. After fault isolation, the fault parameter estimate generated by the matched isolation estimator is used for accommodating the fault effect. Using an indoor quadrotor test environment, real-time experimental results are shown to illustrate the effectiveness of the algorithms.
TL;DR: The main contributions of the proposed fault location technique are to decrease the multiple estimations associated with impedance-based methods, to propose a systematic approach to build the LVZs, and to explore the presence of smart meters for fault location.
Abstract: This paper proposes to combine the voltage monitoring capability of smart meters with impedance-based fault location methods to provide an efficient fault location approach improving service restoration. The first step of the proposed methodology is to apply an impedance-based method to obtain a rough estimation of fault location. Since the result is an estimated distance to the fault, multiple branches can be indicated due to the typical distribution systems topologies. Therefore, the challenge is: how to recognize the actual fault location? To solve this problem, voltage measurements from smart meters are used to build the low voltage zones (LVZs). The main contributions of the proposed fault location technique are to decrease the multiple estimations associated with impedance-based methods, to propose a systematic approach to build the LVZs, and to explore the presence of smart meters for fault location. The proposed method was tested through intensive and extensive simulations in a real distribution system, proving its efficiency.
TL;DR: In this article, the authors proposed a method for real-time monitoring and fault diagnosis in photovoltaic systems, which is based on a comparison between the performances of a faulty PV module, with its accurate model by quantifying the specific differential residue that will be associated with it.
TL;DR: The work presented in this paper involves building an effective fault prediction tool by identifying and investigating the predictive power of several well-known and widely used software metrics for fault prediction by using Least Squares Support Vector Machine learning method associated with linear, polynomial and radial basis function kernel functions.
12 Oct 2017
TL;DR: TraPT, an automated Learning-to-Rank technique to fully explore the obtained mutation information for effective fault localization is proposed, and experimental results show that TraPT localizes 65.12% and 94.52% more bugs within Top-1 than state-of-the-art mutation and spectrum based techniques when using the default setting of LIBSVM.
Abstract: Localizing failure-inducing code is essential for software debugging. Manual fault localization can be quite tedious, error-prone, and time-consuming. Therefore, a huge body of research e orts have been dedicated to automated fault localization. Spectrum-based fault localization, the most intensively studied fault localization approach based on test execution information, may have limited effectiveness, since a code element executed by a failed tests may not necessarily have impact on the test outcome and cause the test failure. To bridge the gap, mutation-based fault localization has been proposed to transform the programs under test to check the impact of each code element for better fault localization. However, there are limited studies on the effectiveness of mutation-based fault localization on sufficient number of real bugs. In this paper, we perform an extensive study to compare mutation-based fault localization techniques with various state-of-the-art spectrum-based fault localization techniques on 357 real bugs from the Defects4J benchmark suite. The study results firstly demonstrate the effectiveness of mutation-based fault localization, as well as revealing a number of guidelines for further improving mutation-based fault localization. Based on the learnt guidelines, we further transform test outputs/messages and test code to obtain various mutation information. Then, we propose TraPT, an automated Learning-to-Rank technique to fully explore the obtained mutation information for effective fault localization. The experimental results show that TraPT localizes 65.12% and 94.52% more bugs within Top-1 than state-of-the-art mutation and spectrum based techniques when using the default setting of LIBSVM.
TL;DR: An online fault detection and classification method is proposed for thermocouples used in nuclear power plants and a technique is proposed to identify the faulty sensor from the fault data.
Abstract: In this paper, an online fault detection and classification method is proposed for thermocouples used in nuclear power plants. In the proposed method, the fault data are detected by the classification method, which classifies the fault data from the normal data. Deep belief network (DBN), a technique for deep learning, is applied to classify the fault data. The DBN has a multilayer feature extraction scheme, which is highly sensitive to a small variation of data. Since the classification method is unable to detect the faulty sensor; therefore, a technique is proposed to identify the faulty sensor from the fault data. Finally, the composite statistical hypothesis test, namely generalized likelihood ratio test, is applied to compute the fault pattern of the faulty sensor signal based on the magnitude of the fault. The performance of the proposed method is validated by field data obtained from thermocouple sensors of the fast breeder test reactor.
TL;DR: 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 based on an experimental study on the practical Internet-based three-tank system.
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.
TL;DR: A Bayesian network-based fault analysis method is proposed, from which novel fault identification, inference, and sensitivity analysis methods are developed and analyzed in a centrifugal compressor utilized in a plant.
Abstract: For high-value assets such as certain types of plant equipment, the total amount of resources devoted to Operation and Maintenance may substantially exceed the resources expended in acquisition and installation of the asset, because high-value assets have long useful lifetimes. Any asset failure during this useful lifetime risks large losses in income and goodwill, and decreased safety. With the continual development of information, communication, and sensor technologies, Condition-Based Maintenance (CBM) policies have gained popularity in industries. A successfully implemented CBM reduces the losses due to equipment failure by intelligently maintaining the equipment before catastrophic failures occur. However, effective CBM requires an effective fault analysis method based on gathered sensor data. In this vein, this paper proposes a Bayesian network-based fault analysis method, from which novel fault identification, inference, and sensitivity analysis methods are developed. As a case study, the fault analysis method was analyzed in a centrifugal compressor utilized in a plant.
TL;DR: A novel symmetrical components (SCs) analysis is utilized to extract the feature of those fault conditions by logically analyzing the pattern of magnitude and phase angle changes of the fundamental signal in the SCs.
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.
20 May 2017
TL;DR: A metric, called DDU, aimed at complementing adequacy measurements by quantifying a test-suite's diagnosability, i.e., the effectiveness of applying spectrum-based fault localization to pinpoint faults in the code in the event of test failures is proposed.
Abstract: Current metrics for assessing the adequacy of a test-suite plainly focus on the number of components (be it lines, branches, paths) covered by the suite, but do not explicitly check how the tests actually exercise these components and whether they provide enough information so that spectrum-based fault localization techniques can perform accurate fault isolation. We propose a metric, called DDU, aimed at complementing adequacy measurements by quantifying a test-suite's diagnosability, i.e., the effectiveness of applying spectrum-based fault localization to pinpoint faults in the code in the event of test failures. Our aim is to increase the value generated by creating thorough test-suites, so they are not only regarded as error detection mechanisms but also as effective diagnostic aids that help widely-used fault-localization techniques to accurately pinpoint the location of bugs in the system. Our experiments show that optimizing a test suite with respect to DDU yields a 34% gain in spectrum-based fault localization report accuracy when compared to the standard branch-coverage metric.
TL;DR: In this paper, a fault diagnosis and compensation problem for two-dimensional discrete time systems with time-varying state delays is studied, and 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.
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.
TL;DR: In this paper, generalized logic-based methods for intelligent fault diagnosis in power electronic converters based on correlation between faults and basic measurements are presented, which can be applied to any power electronic system.
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.
21 Aug 2017
TL;DR: QTEP leverages code inspection techniques, i.e., a typical statistic defect prediction model and a typical static bug finder, to detect fault-prone source code and then adapt existing coverage-based TCP algorithms by considering the weighted source code in terms of fault-proneness.
Abstract: Test case prioritization (TCP) is a practical activity in software testing for exposing faults earlier. Researchers have proposed many TCP techniques to reorder test cases. Among them, coverage-based TCPs have been widely investigated. Specifically, coverage-based TCP approaches leverage coverage information between source code and test cases, i.e., static code coverage and dynamic code coverage, to schedule test cases. Existing coverage-based TCP techniques mainly focus on maximizing coverage while often do not consider the likely distribution of faults in source code. However, software faults are not often equally distributed in source code, e.g., around 80% faults are located in about 20% source code. Intuitively, test cases that cover the faulty source code should have higher priorities, since they are more likely to find faults. In this paper, we present a quality-aware test case prioritization technique, QTEP, to address the limitation of existing coverage-based TCP algorithms. In QTEP, we leverage code inspection techniques, i.e., a typical statistic defect prediction model and a typical static bug finder, to detect fault-prone source code and then adapt existing coverage-based TCP algorithms by considering the weighted source code in terms of fault-proneness. Our evaluation with 16 variant QTEP techniques on 33 different versions of 7 open source Java projects shows that QTEP could improve existing coverage-based TCP techniques for both regression and new test cases. Specifically, the improvement of the best variant of QTEP for regression test cases could be up to 15.0% and on average 7.6%, and for all test cases (both regression and new test cases), the improvement could be up to 10.0% and on average 5.0%.
TL;DR: In this article, a state estimation-based method for fault location in distribution networks using the measurements provided by the smart meters is presented, where the fault is considered as an unknown and temporarily connected load which can be dealt with as bad data.
TL;DR: This paper uses fault injection to populate the database of failures for a target distributed system, and shows that this approach is effective in determining the root causes, e.g., fault types and affected components, for 71-100 percent of tested failures.
Abstract: This paper introduces a novel approach to automating failure diagnostics in distributed systems by combining fault injection and data analytics. We use fault injection to populate the database of failures for a target distributed system. When a failure is reported from production environment, the database is queried to find “matched” failures generated by fault injections. Relying on the assumption that similar faults generate similar failures, we use information from the matched failures as hints to locate the actual root cause of the reported failures. In order to implement this approach, we introduce techniques for (i) reconstructing end-to-end execution flows of distributed software components, (ii) computing the similarity of the reconstructed flows, and (iii) performing precise fault injection at pre-specified executing points in distributed systems. We have evaluated our approach using an OpenStack cloud platform, a popular cloud infrastructure management system. Our experimental results showed that this approach is effective in determining the root causes, e.g., fault types and affected components, for 71-100 percent of tested failures. Furthermore, it can provide fault locations close to actual ones and can easily be used to find and fix actual root causes. We have also validated this technique by localizing real bugs that occurred in OpenStack.
TL;DR: In this article, the authors review the availability of fault location techniques in traditional distribution networks and present a review of the availabability of these techniques with satisfactory accuracy for the last two decades.
Abstract: Electricity customers are usually connected to the power system networks through distribution grids, and any interruption in these grids causes customer minute loss (CML). Most of the CMLs are caused due to different types of faults sustained for a longer period of time. Therefore, rapid fault location techniques are very necessary in order to restore the power supply quickly by reducing outage durations and revenue losses. To expedite restoration process and to ensure reliable power supply in traditional distribution networks, many fault location techniques were presented by researchers with satisfactory accuracy for last two decades. However, the recent trends of adopting distributed generators (DGs) to distribution grids are disturbing grid protection schemes by changing their technical parameters and direction of current/power flows. Additionally, the accuracy of traditional fault lactation techniques is also being affected significantly. The aim of this research paper is to review the availab...
TL;DR: To better resist signature attacks on scan testable cryptochip, it is proposed to fortify the key and lock method by the static obfuscation of scan data, which is unconditionally resilient against TMOSA and all other known scan-based attacks while preserving the merits of high testability and low area overhead compared with other countermeasures.
Abstract: Due to the fallibility of advanced integrated circuit (IC) fabrication processes, scan test has been widely used by cryptographic ICs to provide high fault coverage. Full controllability and observability offered by the scan design also open out the trapdoor to side-channel attacks. To better resist signature attacks on scan testable cryptochip, we propose to fortify the key and lock method by the static obfuscation of scan data. Instead of spatially reshuffling the scan cells, the working mode of some scan cells is altered to jumble up the scan data when the scan test is performed with an incorrect test key. However, when the plaintext is fed directly through the primary inputs for test efficiency, the static obfuscation of scan data is inadequate as demonstrated by a new test-mode-only signature attack (TMOSA) proposed in this paper. To thwart TMOSA, a new countermeasure based on the dynamic obfuscation of scan data is proposed. By cyclically shifting the incorrect test key throughout the test phase, the blocking cells due to the mismatched bits of the test key are made to move temporally to dynamically obfuscate the scan data. This latter scheme is unconditionally resilient against TMOSA and all other known scan-based attacks while preserving the merits of high testability and low area overhead compared with other countermeasures.
TL;DR: 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 and achieves relatively high fault diagnosis and prognosis accuracy.
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.
TL;DR: An intelligent method for fault diagnosis based on fault tree analysis and a fuzzy neural network is presented, which implements diagnosis for most kinds of faults in the aquaculture IoT.
Abstract: In the Internet of Things (IoT) equipment used for aquaculture is often deployed in outdoor ponds located in remote areas. Faults occur frequently in these tough environments and the staff generally lack professional knowledge and pay a low degree of attention in these areas. Once faults happen, expert personnel must carry out maintenance outdoors. Therefore, this study presents an intelligent method for fault diagnosis based on fault tree analysis and a fuzzy neural network. In the proposed method, first, the fault tree presents a logic structure of fault symptoms and faults. Second, rules extracted from the fault trees avoid duplicate and redundancy. Third, the fuzzy neural network is applied to train the relationship mapping between fault symptoms and faults. In the aquaculture IoT, one fault can cause various fault symptoms, and one symptom can be caused by a variety of faults. Four fault relationships are obtained. Results show that one symptom-to-one fault, two symptoms-to-two faults, and two symptoms-to-one fault relationships can be rapidly diagnosed with high precision, while one symptom-to-two faults patterns perform not so well, but are still worth researching. This model implements diagnosis for most kinds of faults in the aquaculture IoT.