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Multiple Fault Diagnosis 2015


According to Shakeri, “the computational complexity of solving the optimal multiple-fault isolation problem is super exponential.”  Most processes and procedures assume that there will be only one fault at any given time.  Many algorithms are designed to do sequential diagnostics.  With the growth of cloud computing and multicore processors and the ubiquity of sensors, the problem of multiple fault diagnosis has grown even larger.  For the Science if Security community, multiple fault diagnosis is relevant to cyber physical systems, resiliency, metrics, and human factors.  The work cited here was presented in 2015.

Zhang Ke; Chai Yi; Liu Jianhuan; Feng Xiaohui, "Analysis of Class Group Distinguishing Based Conceptual Models for Multiple Fault Diagnosis," in Control Conference (CCC), 2015 34th Chinese, pp. 6397-6402, 28-30 July 2015. doi: 10.1109/ChiCC.2015.7260647

Abstract: It is common that multiple fault exists in actual engineering and complex systems. Due to parameters in multiple faults tightly coupled, relationship between the fault mode and known mono-fault features is non-linear. Thus, it is hard to see how distinguish in mapping set for "fault to symptom". In this case, there is no guarantee that traditional diagnosis methods for mono-fault meet the demands. With the requirement, an analysis of the traits of multiple faults is made. A summarization is given to class group distinguishing (CGD) based methods that applied in fault diagnosis. Major defects in the methods that applied in multiple fault diagnosis are analyzed. On that basis, fault modes and symptoms are taken as key points. Conceptual models for multiple fault diagnosis based on CGD are gradually explored. By the models, actual faults can be mapped to one or more known mono-faults via distinguishing analysis, and therefore multiple faults can be diagnosed. There are 4 kinds of flow chart and construction for the models are established. Each of these models presents advantages and disadvantages are separately presented at the end of the chapter.

Keywords: fault diagnosis; pattern classification; pattern clustering; uncertainty handling; CGD based methods; class group distinguishing based conceptual models; fault to symptom; monofault features; multiple fault diagnosis; uncertainty reasoning; Analytical models; Automation; Cognition; Couplings; Fault diagnosis; Support vector machines; Uncertainty; Class Group Distinguishing; Classification; Cluster; Conceptual Models; Fault Diagnosis; Multiple Fault (ID#: 16-9213)



Irita, T.; Namerikawa, T., "Decentralized Fault Detection of Multiple Cyber Attacks in Power Network via Kalman Filter," in Control Conference (ECC), 2015 European pp. 3180-3185, 15-17 July 2015.

doi: 10.1109/ECC.2015.7331023

Abstract: This paper discusses faults diagnosis method of multiple cyber attacks in networked electrical power systems. We deal with a power network of a centralized system, and then, some parameters are estimated by Kalman filter. Using a sensor network system, each of sensor nodes can exchange information by wireless communication. A fault diagnosis method is proposed by using both fault detection diagnosis matrix and fault distinction matrix. The former is composed of estimated values and observed values. On the other hand, the latter is composed of observed values and calculated values via the senor network. Finally, the effectiveness of the proposed approach is validated by a simulation experiment.

Keywords: Kalman filters; fault diagnosis; matrix algebra; power system faults; power system parameter estimation; power system security; wireless sensor networks; Kalman filter; decentralized fault detection diagnosis matrix; fault distinction matrix; faults diagnosis method; information exchange; multiple cyber attacks; networked electrical power systems; parameter estimation; power network; sensor network system; sensor nodes; wireless communication; Fault detection; Generators; Kalman filters; Mathematical model; Power grids; Power system dynamics; Rotors (ID#: 16-9214)



Meera, G.; Geethakumari, G., "A Provenance Auditing Framework for Cloud Computing Systems," in Signal Processing, Informatics, Communication and Energy Systems (SPICES), 2015 IEEE International Conference on, pp. 1-5, 19-21 Feb. 2015. doi: 10.1109/SPICES.2015.7091427

Abstract: Cloud computing is a service oriented paradigm that aims at sharing resources among a massive number of tenants and users. This sharing facility that it provides coupled with the sheer number of users make cloud environments susceptible to major security risks. Hence, security and auditing of cloud systems is of great relevance. Provenance is a meta-data history of objects which aid in verifiability, accountability and lineage tracking. Incorporating provenance to cloud systems can help in fault detection. This paper proposes a framework which aims at performing secure provenance audit of clouds across applications and multiple guest operating systems. For integrity preservation and verification, we use established cryptographic techniques. We look at it from the cloud service providers' perspective as improving cloud security can result in better trust relations with customers.

Keywords: auditing; cloud computing; cryptography; data integrity; fault diagnosis; meta data; resource allocation; service-oriented architecture; trusted computing; accountability; cloud computing systems; cloud environments; cloud security; cloud service providers; cryptographic techniques; fault detection; integrity preservation; integrity verification; lineage tracking; metadata history; operating systems; provenance auditing framework; resource sharing; security risks; service oriented paradigm; sharing facility; trust relations; verifiability; Cloud computing; Cryptography; Digital forensics; Monitoring; Virtual machining; Auditing; Cloud computing; Provenance (ID#: 16-9215)



Sousa, B.; Pentikousis, K.; Curado, M., "Multihoming Aware Optimization Mechanism," in Integrated Network Management (IM), 2015 IFIP/IEEE International Symposium on, pp. 1085-1091, 11-15 May 2015. doi: 10.1109/INM.2015.7140437

Abstract: Network management includes several operations that aim to maximize Fault, Configuration, Account, Performance and Security (FCAPS) goals. Performance improvement often relies on multiple criteria, leading to NP-Hard optimisation problems. Very often, optimization mechanisms are narrowed to a specific scenario or present deployment issues due to their associated complexity. Others, despite reducing complexity, have accuracy issues that lead to the selection of non-optimal solutions. MeTHODICAL is an accurate optimisation technique for path selection in multihoming scenarios that enhances network management FCAPS goals by being flexible enough to operate on distinct scenarios, supporting different applications and services and with reduced deployment complexity.

Keywords: communication complexity; fault diagnosis; optimisation; performance evaluation; telecommunication network management; telecommunication security; FCAPS goal; NP-hard optimisation problem; deployment complexity; fault configuration account performance and security goal; multihoming aware optimization mechanism; multihoming scenario; network management; path selection; performance improvement; Accuracy; Codecs; NP-hard problem; Optimization; Resilience; Servers; FCAPS; Multihoming; optimization; resilience (ID#: 16-9216)



Marijan, D., "Multi-perspective Regression Test Prioritization for Time-Constrained Environments," in Software Quality, Reliability and Security (QRS), 2015 IEEE International Conference on, pp. 157-162, 3-5 Aug. 2015. doi: 10.1109/QRS.2015.31

Abstract: Test case prioritization techniques are widely used to enable reaching certain performance goals during regression testing faster. A commonly used goal is high fault detection rate, where test cases are ordered in a way that enables detecting faults faster. However, for optimal regression testing, there is a need to take into account multiple performance indicators, as considered by different project stakeholders. In this paper, we introduce a new optimal multi-perspective approach for regression test case prioritization. The approach is designed to optimize regression testing for faster fault detection integrating three different perspectives: business perspective, performance perspective, and technical perspective. The approach has been validated in regression testing of industrial mobile device systems developed in continuous integration. The results show that our proposed framework efficiently prioritizes test cases for faster and more efficient regression fault detection, maximizing the number of executed test cases with high failure frequency, high failure impact, and cross-functional coverage, compared to manual practice.

Keywords: fault diagnosis; program testing; regression analysis; business perspective; continuous integration; cross-functional coverage; failure frequency; failure impact; fault detection rate; industrial mobile device system; multiperspective regression test prioritization; optimal multiperspective approach; optimal regression testing; performance indicator; performance perspective; regression fault detection; regression test case prioritization; technical perspective; test case prioritization technique; time-constrained environment; Business; Fault detection; Manuals; Software; Testing; Time factors; Time-frequency analysis; regression testing; software testing; test case prioritization (ID#: 16-9217)



Pomeranz, I., "Improving the Accuracy of Defect Diagnosis with Multiple Sets of Candidate Faults," in Computers, IEEE Transactions on, vol PP, no. 99, pp.1-1, 2015. doi: 10.1109/TC.2015.2468234

Abstract: Given a chip that produced a faulty output response to a test set, a defect diagnosis procedure produces a set of candidate faults that is expected to identify the defects that are present in the chip. The accuracy of the set of candidate faults is higher when the set is smaller or when its overlap with the defects that are present in the chip is larger. To increase the accuracy of a set of candidate faults, this paper describes an approach where several sets of candidate faults are computed based on different subsets of the test set. The subsets are obtained by removing small numbers of tests from the complete test set. The result is sets of candidate faults that are similar but not identical. The number of sets where a fault appears yields a confidence level that the fault actually belongs in a set of candidate faults. New sets of candidate faults are defined based on the confidence levels obtained. The smallest set of candidate faults can be used as the final result of defect diagnosis, or the sets can be used for ranking the candidates. Experimental results for benchmark circuits demonstrate the effectiveness of this approach.

Keywords: Accuracy; Benchmark testing; Circuit faults; Computational modeling; Failure analysis; Fault diagnosis; Integrated circuit modeling; Candidate faults; defect diagnosis; failure analysis; testing; transition faults (ID#: 16-9218)



Zhao, C.; Gao, F., "Fault Subspace Selection Approach Combined With Analysis of Relative Changes for Reconstruction Modeling and Multifault Diagnosis," in Control Systems Technology, IEEE Transactions on, vol. PP, no. 99, pp.1-12, 2015. doi: 10.1109/TCST.2015.2464331

Abstract: Online fault diagnosis has been a crucial task for industrial processes, which in general is taken after some abnormalities have been detected. Reconstruction-based fault diagnosis has been drawing special attention as a good alternative to the traditional contribution plot. It identifies the fault cause by finding the specific reconstruction model (i.e., fault subspace) that can well eliminate alarm signals from a bunch of alternatives that have been prepared based on historical fault data. However, in practice, the abnormality may result from the joint effects of multiple faults, which thus cannot be well corrected by single-fault subspace archived in the historical fault library. In this paper, an aggregative reconstruction-based fault diagnosis strategy is proposed to handle the case where multiple-fault causes jointly contribute to the abnormal process behaviors. First, fault subspaces are extracted based on historical fault data in two different monitoring subspaces where analysis of relative changes is taken to enclose the major fault effects that are responsible for different alarm monitoring statistics. Then, a fault subspace selection strategy is developed to analyze the combinatorial fault nature that will sort and select the informative fault subspaces by evaluating their significances in data correction. Finally, an aggregative fault subspace is calculated by combining the selected fault subspaces, which represents the joint effects from multiple faults and works as the final reconstruction model for online fault diagnosis. Theoretical support is framed and the related statistical characteristics are analyzed. Its feasibility and performance are illustrated with simulated multiple faults from the Tennessee Eastman benchmark process.

Keywords: Correlation; Data models; Fault diagnosis; Joints; Libraries; Monitoring; Principal component analysis; Analysis of relative changes; fault subspace selection; joint fault effects; multifault (MF) diagnosis; reconstruction modeling (ID#: 16-9219)



Bahadorinejad, A.; Braga-Neto, U., "Optimal Fault Detection and Diagnosis in Transcriptional Circuits using Next-Generation Sequencing," in Computational Biology and Bioinformatics, IEEE/ACM Transactions on  vol. PP, no. 99, pp.1-1, 2015. doi: 10.1109/TCBB.2015.2404819

Abstract: We propose a methodology for model-based fault detection and diagnosis for stochastic Boolean dynamical systems indirectly observed through a single time series of transcriptomic measurements using Next Generation Sequencing (NGS) data. The fault detection consists of an innovations filter followed by a fault certification step, and requires no knowledge about the system faults. The innovations filter uses the optimal Boolean state estimator, called the Boolean Kalman Filter (BKF). In the presence of knowledge about the possible system faults, we propose an additional step of fault diagnosis based on a multiple model adaptive estimation (MMAE) method consisting of a bank of BKFs running in parallel. Performance is assessed by means of false detection and misdiagnosis rates, as well as average times until correct detection and diagnosis. The efficacy of the proposed methodology is demonstrated via numerical experiments using a p53-MDM2 negative feedback loop Boolean network with stuck-at faults that model molecular events commonly found in cancer.

Keywords: Bioinformatics; Computational biology; DNA; Fault detection; IEEE transactions; Technological innovation; Vectors; Boolean Kalman Filter; Boolean Networks; Fault Detection and Diagnosis; Next Generation Sequencing; Optimal Estimation (ID#: 16-9220)



Zhou, J.; Chen, Z.; Wang, J.; Zheng, Z.; Lyu, M.R., "A Data Set for User Request Trace-Oriented Monitoring and its Applications," in Services Computing, IEEE Transactions on, vol. PP, no. 99, pp.1-1, 2015. doi: 10.1109/TSC.2015.2491286

Abstract: User request trace-oriented monitoring is an effective method to improve the reliability of cloud services. However, there are some difficulties in getting useful traces in practice, which hinder the development of trace-oriented monitoring research. In this paper, we release a fine-grained user request-centric open trace data set, called TraceBench, which is collected in a real-world cloud storage service deployed in a real environment. When collecting, we consider different scenarios, involving multiple scales of clusters, different kinds of user requests, various speeds of workloads, many types of injected faults, etc. To validate the usability and authenticity, we have employed TraceBench in several trace-oriented monitoring topics, such as anomaly detection, performance problem diagnosis, and temporal invariant mining. The results show that TraceBench well supports these research topics. In addition, we have also carried out an extensive data analysis based on TraceBench, which validates the high quality of the data set.

Keywords: Cloud computing; Data mining; IP networks; Instruments; Monitoring; Reliability; Servers; anomaly detection; cloud services; data set; end-to-end tracing; trace-oriented monitoring (ID#: 16-9221)



Jiang, Y.; Liu, C.-C.; Diedesch, M.; Lee, E.; Srivastava, A.K., "Outage Management of Distribution Systems Incorporating Information From Smart Meters," in Power Systems, IEEE Transactions on, vol. PP, no. 99, pp.1-11, 2015. doi: 10.1109/TPWRS.2015.2503341

Abstract: A critical function in outage management for distribution systems is to quickly detect a fault and identify the activated protective device(s). With ongoing smart grid development, numerous smart meters and fault indicators with communication capabilities provide an opportunity for accurate and efficient outage management. Using the available data, this paper proposes a new multiple-hypothesis method for identification of the faulted section on a feeder or lateral. Credibility of the multiple hypotheses is determined using the available evidence from these devices. The proposed methodology is able to handle i) multiple faults, ii) protection miscoordination, and iii) missing outage reports from smart meters and fault indicators. For each hypothesis, an optimization method based on integer programming is proposed to determine the most credible actuated protective device(s) and faulted line section(s). Simulation results based on the distribution feeders of Avista Utilities serving Pullman, WA, validate the effectiveness of the proposed approach.

Keywords: Automation; Fault location; Fuses; Smart meters; Substations; Topology; Distribution automation; fault diagnosis; fault indicator; multiple hypotheses; outage management; smart meter (ID#: 16-9222)



Wang, H.; Yang, G.; Ye, D., "Fault Detection and Isolation for Affine Fuzzy Systems with Sensor Faults," in Fuzzy Systems, IEEE Transactions on, vol. PP, no. 99, pp.1-1, 2015. doi: 10.1109/TFUZZ.2015.2501414

Abstract: This paper investigates the fault detection and isolation (FDI) problem for a class of nonlinear systems with sensor outage faults. The considered nonlinear systems are described as affine fuzzy models, and the system outputs are chosen as the premise variables of fuzzy models. Different from the existing results, the influence of sensor faults on premise variables is considered. As a result, the well-known parallel distributed compensation (PDC) scheme cannot be used for FDI filters design. By using the structural information encoded in the fuzzy rules, the affine fuzzy system is represented by multiple operating-regime-based models in fault-free case and faulty cases. In the multiple-model scheme, a bank of piecewise FDI filters are constructed, each of them is based on the affine fuzzy model that describes the system in the presence of a specified fault. The fault-dependent residual signals generated from the filters are used for detecting and isolating the specified fault. The FDI filter design conditions are obtained in the formulation of linear matrix inequalities (LMIs). Finally, a numerical example is given to illustrate the effectiveness and merits of the proposed method.

Keywords: Control systems; Fault detection; Fault diagnosis; Fuzzy systems; Indexes; Interpolation; Nonlinear systems; Affine fuzzy systems; fault detection and isolation; linear matrix inequalities; sensor outage faults (ID#: 16-9223)



Pourbabaee, B.; Meskin, N.; Khorasani, K., "Sensor Fault Detection, Isolation, and Identification Using Multiple-Model-Based Hybrid Kalman Filter for Gas Turbine Engines," in Control Systems Technology, IEEE Transactions on, vol. PP, no. 99, pp.1-17, 2015. doi: 10.1109/TCST.2015.2480003

Abstract: In this paper, a novel sensor fault detection, isolation, and identification (FDII) strategy is proposed using the multiple-model (MM) approach. The scheme is based on multiple hybrid Kalman filters (MHKFs), which represents an integration of a nonlinear mathematical model of the system with a number of piecewise linear (PWL) models. The proposed fault detection and isolation (FDI) scheme is capable of detecting and isolating sensor faults during the entire operational regime of the system by interpolating the PWL models using a Bayesian approach. Moreover, the proposed MHKF-based FDI scheme is extended to identify the magnitude of a sensor fault using a modified generalized likelihood ratio method that relies on the healthy operational mode of the system. To illustrate the capabilities of our proposed FDII methodology, extensive simulation studies are conducted for a nonlinear gas turbine engine. Various single and concurrent sensor fault scenarios are considered to demonstrate the effectiveness of our proposed online hierarchical MHKF-based FDII scheme under different flight modes. Finally, our proposed hybrid Kalman filter (HKF)-based FDI approach is compared with various filtering methods such as the linear, extended, unscented, and cubature Kalman filters corresponding to both interacting and noninteracting MM-based schemes. Our comparative studies confirm the superiority of our proposed HKF method in terms of promptness of the fault detection, lower false alarm rates, as well as robustness with respect to the engine health parameter degradations.

Keywords: Degradation; Engines; Fault detection; Kalman filters; Monitoring; Robustness; Turbines; Fault diagnosis; Gas turbine engines; Generalized likelihood ratio; Hybrid Kalman filter; Multiple model-based approach; Piecewise linear models interpolation (ID#: 16-9224)



Xiaoqin, Liu; Zewei, Dong; Hongdong, Qu; Limei, Song, "Dynamic Multiple Fault Diagnosis Based on HMM and BPSO," in Instrumentation and Measurement, Computer, Communication and Control (IMCCC), 2015 Fifth International Conference on, pp. 186-191, 18-20 Sept. 2015. doi: 10.1109/IMCCC.2015.46

Abstract: For systems needed short diagnosis delay, dynamic multiple faults diagnosis (DMFD) is put forward for the systems of demanding diagnosis quickly. In this paper, a method of DMFD based Hidden Markov Mode (HMM) is established for the system's inner states transform and the corresponding external observing sequence, thus the inner states transform could be recovered from the external observing sequence with the decoding algorithm of HMM, which belongs to NP completeness problems. This paper decomposes original DMFD problem into several separable sub problems, and solves each of them with binary particle swarm optimization algorithm (BPSO). It is shown from the application examples that the system's real-time health status could be evaluated at a high correct ratio with this method.

Keywords: Computers; Fault diagnosis; Heuristic algorithms; Hidden Markov models; Linear programming; Markov processes; Particle swarm optimization; BPSO; Hidden Markov Model; dynamic multiple fault diagnosis (ID#: 16-9225)



Tanwir, S.; Prabhu, S.; Hsiao, M.; Lingappan, L., "Information-Theoretic and Statistical Methods of Failure Log Selection for Improved Diagnosis," in Test Conference (ITC), 2015 IEEE International, pp. 1-10, 6-8 Oct. 2015. doi: 10.1109/TEST.2015.7342381

Abstract: Diagnosis of each failed part requires the failed data captured on the test equipment. However, due to memory limitations on the tester, one often cannot store all the failed data for every chip tested. Consequently, truncated failure logs are used instead of complete logs for each part. Such truncation of the failure logs can result in very long turn-around times for diagnosis because important failure points may be removed from the log. Subsequently, the accuracy and resolution of final diagnosis may suffer even after multiple iterations of diagnosis. In addition, the existing test response compaction techniques though good for testing, either adversely affect diagnosis or are highly sensitive to deviation from the chosen fault model. In this context, the industry needs dynamic selection of better failure logs that enhances diagnosis. In this paper, we propose a number of metrics based on information theory that may help in selecting failure logs dynamically for improving the accuracy and resolution of final diagnosis. We also report on the efficacy of these metrics through the results of our experiments.

Keywords: failure analysis; fault diagnosis; information theory; iterative methods; microprocessor chips; statistical analysis; every tested chip; failed data; failure log selection; final diagnosis; improved diagnosis; information-theoretic methods; memory limitations; multiple diagnosis iterations; statistical methods; test equipment; test response compaction; truncated failure logs; Circuit faults; Compaction; Industries; Integrated circuit modeling; Measurement; Real-time systems; Testing (ID#: 16-9226)



Jie Zhang; Ming Lyu; Xianfeng Li; Jiping Zheng, "Fault Detection for Networked Control System with Multiple Communication Delays and Multiple Missing Measurements," in Networking, Sensing and Control (ICNSC), 2015 IEEE 12th International Conference on, pp. 580-585, 9-11 April 2015. doi: 10.1109/ICNSC.2015.7116102

Abstract: This paper is concerned with fault detection problem for a class of Networked Control Systems (NCSs) with both multiple Communication Delays and multiple missing measurements. In this paper we build up a fault detection filter through a model of NCS, so that the overall fault detection dynamics is exponentially stable in the mean square, besides the error between the residual signal and the fault signal is made as small as possible. Sufficient conditions are first established for the existence of the desired fault detection filters, and then, the corresponding solvability conditions for the desired filter gains are established. At the end of this title, a simulation example was given to demonstrate the effectiveness of the proposed method.

Keywords: asymptotic stability; delays; fault diagnosis; networked control systems; NCS; exponential stability; fault detection; multiple communication delays; multiple missing measurements; networked control system; sufficient conditions; Delays; Electronic mail; Fault detection; Loss measurement; Networked control systems; Noise; Random variables (ID#: 16-9227)



Timotheou, S.; Panayiotou, C.; Polycarpou, M., "Fault-Adaptive Traffic Density Estimation for the Asymmetric Cell Transmission Model," in Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on, pp. 2855-2860, 15-18 Sept. 2015. doi: 10.1109/ITSC.2015.459

Abstract: The often faulty nature of measurement sensors hinders reliable traffic state estimation, affecting in this way various transportation operations such as traffic control and in-car navigation. This work proposes a systematic, model-based, online and network-wide approach to achieve robust and good quality state estimation in the presence of sensor faults. The approach is comprised of three stages aiming at 1) identifying the level of faulty behavior of each sensor using a novel fault-tolerant optimization algorithm, 2) isolating faults, and 3) improving state estimation performance by adaptively compensating sensor faults and resolving the state estimation problem. The approach is examined in the context of the Asymmetric Cell Transmission Model for freeway traffic density estimation. Simulation results demonstrate the effectiveness of the proposed fault-adaptive approach, yielding estimation performance very close to the one obtained with healthy measurements, irrespective of the fault magnitude. It is further illustrated that the developed fault-tolerant optimization algorithm can simultaneously identify different types of faults from multiple sensors.

Keywords: estimation theory; fault diagnosis fault tolerant control; optimisation; road traffic control; state estimation; asymmetric cell transmission model; fault magnitude; fault-adaptive approach; fault-adaptive traffic density estimation; fault-tolerant optimization algorithm; faulty behavior; freeway traffic density estimation; healthy measurement; in-car navigation; isolating fault; measurement sensor; quality state estimation; sensor fault; state estimation performance; state estimation problem; traffic control; traffic state estimation; transportation operation; Fault detection; Fault tolerance; Noise; Noise measurement; Sensors; State estimation (ID#: 16-9228)



Kumar, T.N.; Almurib, H.A.F.; Lombardi, F., "Operational Fault Detection and Monitoring of a Memristor-Based LUT," in Design, Automation & Test in Europe Conference & Exhibition (DATE), 2015, pp.429-434, 9-13 March 2015. Doi:  (not provided)

Abstract: This paper presents a method for operational testing of a memristor-based memory look-up table (LUT). In the proposed method, the deterioration of the memristors (as storage elements of a LUT) is modeled based on the reduction of the resistance range as observed in fabricated devices and recently reported in the technical literature. A quiescent current technique is used for testing the memristors when deterioration results in a change of state, thus leading to an erroneous (faulty) operation. An equivalent circuit model of the operational deterioration for a memristor-based LUT is presented. In addition to modeling and testing, the proposed method can be utilized also for continuous monitoring of the LUT in the presence of memristor deterioration in the LUT. The proposed method is assessed using LTSPICE; extensive simulation results are presented with respect to different operational features, such as LUT dimension and range of resistance. These results show that the proposed test method is scalable with LUT dimension and highly efficient for testing and monitoring a LUT in the presence of deteriorating multiple memristors.

Keywords: SPICE; equivalent circuits; fault diagnosis; memristors; table lookup; LTSPICE; change of state; device fabrication equivalent circuit model; memristor operational deterioration; memristor-based LUT dimension; memristor-based memory lookup table; operational fault detection; operational fault monitoring; operational testing; quiescent current technique; resistance range reduction; technical literature; Circuit faults; Current measurement; Electrical resistance measurement; Memristors; Resistance; Table lookup; Testing; Memristor; deterioration; monitoring; quiescent current ;testing (ID#: 16-9229)



Wyzga, A.; Gruca, J.; Polit, A.; Papafotiou, G., "The Load Current Sensing Method in the Multiple Output High Insulation Voltage Transformer," in Power Electronics and Applications (EPE'15 ECCE-Europe), 2015 17th European Conference on, pp. 1-8, 8-10 Sept. 2015. doi: 10.1109/EPE.2015.7309100

Abstract: Some power supplies do not require accurate current feedback to function properly. In many cases the information about the output current is only needed to make sure that the power supply is not overloaded. This paper describes a sensing method of individual secondary currents of a four channel power supply built using a single core transformer, by sensing the currents flowing in separate primary windings. Each secondary current could be sensed on the primary side of the high insulation voltage transformer with sufficient accuracy to assure proper fault handling strategy for the power supply.

Keywords: electric sensing devices; fault diagnosis; power supplies to apparatus; power transformer insulation; transformer cores; transformer windings; fault handling strategy; four channel power supply; load current sensing method; multiple output high insulation voltage transformer; primary winding; secondary current; single core transformer; Couplings; Insulation; Magnetic resonance; Power supplies; Power transformer insulation; Sensors; Windings; breakdown; circuits; converter control; current sensor; fault handling strategy; insulation; load sharing control; measurement; power supply (ID#: 16-9230)



Indhumathi, C.; Vasantha Rani, S.P.J., "A Fuzzy Based Fault Type Detector for Remote Fault Diagnosis of Distribution Feeders," in Signal Processing, Communication and Networking (ICSCN), 2015 3rd International Conference on, pp. 1-5, 26-28 March 2015. doi: 10.1109/ICSCN.2015.7219832

Abstract: Increasing energy demands has led to expansion of power infrastructure which also means that there is an increase in the number of lines subjected to faults due to short circuits or unintentional causes such as birds, falling of branches, etc,. Sometimes this causes an outage of power. Identification of the right fault type is necessary for quick power restoration. Hence accurate fault classification in power distribution substation is essential. The work presented in the paper aims to automate the fault type identification process using a fuzzy based algorithm thereby reducing the time required for power restoration. The experimental results indicate that the algorithm accurately detects the type of fault in single and multiple fault scenarios.

 Keywords: fault diagnosis; fuzzy logic; power distribution faults; power distribution reliability; power engineering computing; power system restoration; short-circuit currents; substations; distribution feeder; fault Identification; fault classification; fuzzy based fault type detector; multiple fault scenario; power distribution substation; power infrastructure expansion; power outage; power restoration; remote fault diagnosis; short-circuit current; Analytical models; Current measurement; Fault diagnosis; Substations; Training; Distribution substation Reliability; Faults in power system; Fuzzy Logic (ID#: 16-9231)



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