Fault Management Research Articles

Health Management for Self-Aware SoCs Based on IEEE 1687 Infrastructure

Editor’s note: Motivated by the need to tolerate faults, this paper presents a complete fault management solution that includes fault detection and categorization, maintaining a map of faults, and modified scheduling and application algorithms for using healthy resources only. As the system maintains fairly sophisticated models of itself regarding faulty and healthy resources, it constitutes a good example of specialized self-awareness. —Axel Jantsch, TU Wien

Experimental investigation into the fault response of superconducting hybrid electric propulsion electrical power system to a DC rail to rail fault

Hybrid electric propulsion aircraft are proposed to improve overall aircraft efficiency, enabling future rising demands for air travel to be met. The development of appropriate electrical power systems to provide thrust for the aircraft is a significant challenge due to the much higher required power generation capacity levels and complexity of the aero-electrical power systems (AEPS). The efficiency and weight of the AEPS is critical to ensure that the benefits of hybrid propulsion are not mitigated by the electrical power train. Hence it is proposed that for larger aircraft (~200 passengers) superconducting power systems are used to meet target power densities. Central to the design of the hybrid propulsion AEPS is a robust and reliable electrical protection and fault management system. It is known from previous studies that the choice of protection system may have a significant impact on the overall efficiency of the AEPS. Hence an informed design process which considers the key trades between choice of cable and protection requirements is needed. To date the fault response of a voltage source converter interfaced DC link rail to rail fault in a superconducting power system has only been investigated using simulation models validated by theoretical values from the literature. This paper will present the experimentally obtained fault response for a variety of different types of superconducting tape for a rail to rail DC fault. The paper will then use these as a platform to identify key trades between protection requirements and cable design, providing guidelines to enable future informed decisions to optimise hybrid propulsion electrical power system and protection design.

Fault and performance management in multi-cloud based NFV using shallow and deep predictive structures

Deployment of network function virtualization (NFV) over multiple clouds accentuates its advantages such as flexibility of virtualization, proximity to customers and lower total cost of operation. However, NFV over multiple clouds has not yet attained the level of performance to be a viable replacement for traditional networks. One of the reasons is the absence of a standard based fault, configuration, accounting, performance and security (FCAPS) framework for the virtual network services. In NFV, faults and performance issues can have complex geneses within virtual resources as well as virtual networks and cannot be effectively handled by traditional rule-based systems. To tackle the above problem, we propose a fault detection and localization model based on a combination of shallow and deep learning structures. Relatively simpler detection has been effectively shown to be handled by shallow machine learning structures such as support vector machine (SVM). Deeper structure, i.e., the stacked autoencoder has been found to be useful for a more complex localization function where a large amount of information needs to be worked through to get to the root cause of the problem. We provide evaluation results using a dataset adapted from fault datasets available on Kaggle and another based on multivariate kernel density estimation and Markov sampling.

Fault Management System of LP-EGR Using In-Cylinder Pressure Information in Light-Duty Diesel Engines

Particulate matters (PM) accumulation through a low-pressure exhaust gas recirculation (LP-EGR) path may hinder to obtain the desired LP-EGR rate and thus causes an increase of nitrogen oxides (NOx). The degree of lack of the LP-EGR rate should be detected, i.e., an LP-EGR fault, and a remedy to compensate for the lack of LP-EGR rate should be a mandate to suppress NOx emission, i.e., a fault management. In order to accomplish those objectives, this paper proposes an LP-EGR fault management system, which consists of a fault diagnosis algorithm, fault-tolerant control algorithm, and an LP-EGR rate model. The model applies a combustion parameter derived from in-cylinder pressure information to the conventional orifice valve model. Consequently, the LP-EGR rate estimation was improved to the maximum error of 2.38% and root-mean-square-error (RMSE) of 1.34% at various operating conditions even under the fault condition compared to that of the conventional model with the maximum error of 7.46% and RMSE of 5.39%. Using this LP-EGR rate model as a virtual sensor, the fault diagnosis algorithm determines an LP-EGR fault state. Based on the state, the fault-tolerant control determines whether or not to generate the offset of the exhaust throttle valve (ETV) position. This offset combines with the look-up table (LUT)-based feedforward controller to control an LP-EGR rate. As a result of real-time verification of the fault management system in the fault condition, the NOx emission decreased by up to about 15%.

Vehicle fault diagnostics and management system

This project is a kind of advanced automatic identification technology, and is more and more widely used in the fields of transportation and logistics. It looks over the main functions with like Vehicle management, Vehicle Speed limit and Control. This system starts with authentication process to keep itself secure. Here we connect sensors to the STM32 board which in turn is connected to the car through Ethernet cable, as Ethernet in capable of sending large amounts of data at high speeds. This technology involved clearly shows how a careful combination of software and hardware can produce an extremely cost-effective solution to a problem.

Assembling SIP-based VoLTE Call Data Records based on network monitoring

The Voice over LTE (VoLTE) service is under deployment at many operators. Although it has great promises--from high service flexibility to previously unseen QoS measures--so far it poses more challenges than expected. The serving architecture is diverse, and the flexible services mean variable use-cases with complex message sequence charts. In order to be able to control the procedures--during deployment as well as during the operation--we need to have proper network and service management in place. Such a management framework includes network and service monitoring tools that help to provide better visibility of the various procedures. For the VoLTE service, both the LTE Evolved Packet Core (EPC) and the IP Multimedia Subsystem (IMS) need to be monitored. Network-wide data capture and analysis for the EPC and the IMS require new processing methods. These would allow operators to correlate control and user plane information of various interfaces and protocols. There are many obstacles to overcome here, including ciphered control messages and global identifiers hidden by temporary ones. This paper presents a system for SIP Call Data Record assembling, shows what kind of key parameters have to be extracted in order to enable expert analysis. After going into the details of messages, transactions, and dialogues, we present Call Data Record assembling methods for various scenarios. Furthermore, we present methods on how to gather cross-correlated data on specific fault management use-cases, particularly for unsuccessful voice calls.

Causality and Temporal Dependencies in the Design of Fault Management Systems

Reasoning about causes and effects naturally arises in the engineering of safety-critical systems. A classical example is Fault Tree Analysis, a deductive technique used for system safety assessment, whereby an undesired state is reduced to the set of its immediate causes. The design of fault management systems also requires reasoning on causality relationships. In particular, a fail-operational system needs to ensure timely detection and identification of faults, i.e. recognize the occurrence of run-time faults through their observable effects on the system. Even more complex scenarios arise when multiple faults are involved and may interact in subtle ways. In this work, we propose a formal approach to fault management for complex systems. We first introduce the notions of fault tree and minimal cut sets. We then present a formal framework for the specification and analysis of diagnosability, and for the design of fault detection and identification (FDI) components. Finally, we review recent advances in fault propagation analysis, based on the Timed Failure Propagation Graphs (TFPG) formalism.

AUTOMATED FAULT DETECTION, DIAGNOSIS AND MANAGEMENT OF WSN

AUTOMATED FAULT DETECTION, DIAGNOSIS AND MANAGEMENT OF WSN

Scaling expertise via utilisation of an outsourced low-voltage fault management business model

Scaling expertise via utilisation of an outsourced low-voltage fault management business model

De-centralised working for outage management, including auto-dispatch

The traditional model of fault management across electricity distribution networks operators relies on a large team of centralised incident controllers, who can be overloaded during busy periods, and under-utilised during quiet times. Based on analysis undertaken at SP Energy Networks (SPEN), as well as the availability of cutting-edge technology, a new model of de-centralised dispatch management is being implemented which is based on enabling technologies for: (i) Automatic reporting of current field crew positions to the central dispatch centre. (ii) Intelligent grouping of calls by the outage management system (OMS). (iii) Automatic dispatch of outages to field crews by the OMS system. (iv) Equipping of field crews with mobile devices allowing for autonomous status reporting.

A Current Limiting Strategy to Improve Fault Ride-Through of Inverter Interfaced Autonomous Microgrids

With high penetration of distributed energy resources, fault management strategy is of great importance for the distribution network operation. The objective of this paper is to propose a current and voltage limiting strategy to enhance fault ride-through (FRT) capability of inverter-based islanded microgrids (MGs) in which the effects of inverter control system and inverter topology (four/three-wire) are considered. A three-phase voltage-sourced inverter with multi-loop control system implemented in synchronous, stationary, and natural reference frames is employed in this paper for both four- and three-wire configurations. The proposed strategy provides high voltage and current quality during overcurrent conditions, which is necessary for sensitive loads. Several time-domain simulation studies are conducted to investigate the FRT capability of the proposed strategy against both asymmetrical and symmetrical faults. Moreover, the proposed method is tested on the CIGRE benchmark MG to demonstrate the effectiveness of the proposed limiting strategy.

Resilience Design Patterns: A Structured Approach to Resilience at Extreme Scale

Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. While the HPC community has developed various resilience solutions, the solution space remains fragmented. There are no formal methods and metrics to integrate the various HPC resilience techniques into composite solutions, nor are there methods to holistically evaluate the adequacy and efficacy of such solutions in terms of their protection coverage, and their performance & power efficiency characteristics. In this paper, we develop a structured approach to the design, evaluation and optimization of HPC resilience using the concept of design patterns. A design pattern is a general repeatable solution to a commonly occurring problem. We identify the problems caused by various types of faults, errors and failures in HPC systems and the techniques used to deal with these events. Each well-known solution that addresses a specific HPC resilience challenge is described in the form of a pattern. We develop a complete catalog of such resilience design patterns, which may be used as essential building blocks when designing and deploying resilience solutions. We also develop a design framework that enhances a designer's understanding the opportunities for integrating multiple patterns across layers of the system stack and the important constraints during implementation of the individual patterns. It is also useful for defining mechanisms and interfaces to coordinate flexible fault management across hardware and software components. The overall goal of this work is to establish a systematic methodology for the design and evaluation of resilience technologies in extreme-scale HPC systems that keep scientific applications running to a correct solution in a timely and cost-efficient manner despite frequent faults, errors, and failures of various types.

Implementasi Fault Management (Manajemen Kesalahan) Pada Network Management System (NMS) Berbasis SNMP

The use of computer network technology to meet the needs of information quickly and diverse, resulting in the increasingly widespread and increasing complexity of a computer network. Therefore, we need a supporting facility to be able to supervise the network element that is by using Network Management System (NMS). The NMS model established by ISO includes 5 conceptual areas, there are Fault, Configuration, Accounting, Performance and Security Management (FCAPS). One area that will be implemented in this research is Fault Management. The mechanism that will be used for error management is by detecting the occurrence of errors and sending notifications via email when the error appears, then logging report and make a diagnosis and to further recommend the solution to the error. The type of error reported in this system is errors when there is a failure of connectivity on the network device, errors due to services that are not running and not in accordance with the request, errors when high RAM usage, CPU load increases and the use of hard drives which started full. From the research results obtained, built NMS can send error notification and give solution recommendation via email to the network admin so if there is a network error, Fault Management activities can be done as early as possible so that the problem does not result in the deterioration of network quality. Kata kunci -- Network Management ; NMS; Fault Management ; SNMP; System Information

Pareto optimization technique in actuation control for error minimization and reliability analysis in an operational pin-constrained digital microfluidic biochip

Droplet routing in digital microfluidic biochip (DMFB) attempts to reduce overall droplet routing time. This phenomena increases repeated use of electrodes during electro-wetting on dielectrics (EWOD). Repeated use of same electrode requires frequent actuation. Often, this over utilization and frequent actuation of same electrodes affects the reliability of DMFB since these electrodes become fault prone under excessive use. Research on fault probability reduction, fault identification and fault management are key issues to achieve reliable output from DMFB. In this paper, a fault management process is focused with the objective to gain reliable performance of DMFB. This method reduces error probabilities of DMFB through a balanced utilization and actuation of electrodes. Initial phase of the proposed work captures the utilization of different electrodes resulting from a pin-constrained droplet routing schedule of DMFB. This initial routing schedule is then reviewed to ensure equitable utilization of electrodes. This will also increase the life-span of electrodes. Consequently, that will lessen fault probability and improve reliability. Finally a Pareto optimal analysis is performed upon the data acquired from previous two processes. These studies have been experimented upon different benchmark samples including in-vitro test suites and complex assay samples like Bradford protein assay, qPCR, sucrose gradient analysis to test its robustness and the results are really encouraging.

Assessing the potential of MV automation for distribution network reliability improvement

Summary Distribution network automation is a key functionality in the evolution towards smarter electricity grids. The main driver to deploy this kind of smart grid solutions is the improvement of continuity of supply for network users; thanks to the fact that an enhanced network monitoring and telecontrol can improve the fault management process conducted by distribution system operators. However, the parameters that affect their successful implementation are not completely understood yet. Addressing this gap, this paper presents a detailed analysis to identify the main factors affecting the implementation of automation at medium voltage distribution networks and discusses the regulatory implications. Several realistic case studies are analyzed, corresponding to a number distribution networks that resemble the architectural characteristics representative of the European distribution grids in different contexts. Hence, the results allow deriving general conclusions that can be widely applicable to European distribution networks.

Ranking Causal Anomalies for System Fault Diagnosis via Temporal and Dynamical Analysis on Vanishing Correlations

Detecting system anomalies is an important problem in many fields such as security, fault management, and industrial optimization. Recently, invariant network has shown to be powerful in characterizing complex system behaviours. In the invariant network, a node represents a system component and an edge indicates a stable, significant interaction between two components. Structures and evolutions of the invariance network, in particular the vanishing correlations, can shed important light on locating causal anomalies and performing diagnosis. However, existing approaches to detect causal anomalies with the invariant network often use the percentage of vanishing correlations to rank possible casual components, which have several limitations: (1) fault propagation in the network is ignored, (2) the root casual anomalies may not always be the nodes with a high percentage of vanishing correlations, (3) temporal patterns of vanishing correlations are not exploited for robust detection, and (4) prior knowledge on anomalous nodes are not exploited for (semi-)supervised detection. To address these limitations, in this article we propose a network diffusion based framework to identify significant causal anomalies and rank them. Our approach can effectively model fault propagation over the entire invariant network and can perform joint inference on both the structural and the time-evolving broken invariance patterns. As a result, it can locate high-confidence anomalies that are truly responsible for the vanishing correlations and can compensate for unstructured measurement noise in the system. Moreover, when the prior knowledge on the anomalous status of some nodes are available at certain time points, our approach is able to leverage them to further enhance the anomaly inference accuracy. When the prior knowledge is noisy, our approach also automatically learns reliable information and reduces impacts from noises. By performing extensive experiments on synthetic datasets, bank information system datasets, and coal plant cyber-physical system datasets, we demonstrate the effectiveness of our approach.

Lightweight Boolean Network Tomography Based on Partition of Managed Networks

Boolean network tomography is a promising technique to achieve fault management in networks where the existing IP-based troubleshooting mechanism cannot be used. Aiming to apply Boolean network tomography to fault management, a variety of heuristic methods for configuring monitoring trails and paths have been proposed to localize link failures in managed networks. However, these existing heuristic methods must be executed in a centralized server that administers the entire managed network and the methods present scalability problems when applied to large-scale managed networks. Thus, this paper proposes a novel scheme for achieving lightweight Boolean network tomography in a decentralized manner. The proposed scheme partitions the managed network into multiple management areas and localizes link failures independently within each area. This paper also proposes a heuristic network partition method with the aim of efficiently implementing the proposed scheme. The effectiveness of the proposed scheme is verified using typical fault management scenarios where all single-link failures and all dual-link failures are localized by the least number of monitoring paths on predetermined routes. Simulation results show that the proposed scheme can greatly reduce the computational load on the fault management server when Boolean network tomography is deployed in large-scale managed networks. Furthermore, the degradation of optimality in the proposed scheme can be mitigated in comparison with a centralized scheme that utilizes heuristics to reduce the computational load on the centralized server.

Fundamentals of Power Supply Design [Book Review

This book addresses the fundamentals of power supply design. It provides a comprehensive collection of information drawn from the knowledge generated through 40 years of power supply design seminars presented by the author, in cooperation with other expert designers, when working for Unitrode and Texas Instruments (TI). The book This book has 13 chapters totaling 333 pages. At the end of each chapter, except Chapter 1, there are references to seminars from which material in the book was drawn. Some of the topics covered include: industry definitions; process of voltage regulation; switching power topologies; control of power systems; feedback control; voltage measurement; magnetic components; circuit requirements; electromagnetic interference in switching systems; fault management systems; standards development; digital control systems; and power supply construction systems. The author presents straightforward explanations on all aspects of design with just enough math and ample illustrations to communicate how power circuits, topologies, and control methods work, along with the essentials of the many subdisciplines.

A Data Analytic Approach to Automatic Fault Diagnosis and Prognosis for Distribution Automation

Distribution automation (DA) is deployed to reduce outages and to rapidly reconnect customers following network faults. Recent developments in DA equipment have enabled the logging of load and fault event data, referred to as “pick-up activity.” This pick-up activity provides a picture of the underlying circuit activity occurring between successive DA operations over a period of time and has the potential to be accessed remotely for off-line or on-line analysis. The application of data analytics and automated analysis of this data supports reactive fault management and post fault investigation into anomalous network behavior. It also supports predictive capabilities that identify when potential network faults are evolving and offers the opportunity to take action in advance in order to mitigate any outages. This paper details the design of a novel decision support system to achieve fault diagnosis and prognosis for DA schemes. It combines detailed data from a specific DA device with rule-based, data mining, and clustering techniques to deliver the diagnostic and prognostic functions. These are applied to 11-kV distribution network data captured from Pole Mounted Auto-Reclosers as provided by a leading U.K. network operator. This novel automated analysis system diagnoses the nature of a circuit’s previous fault activity, identifies underlying anomalous circuit activity, and highlights indications of problematic events gradually evolving into a full scale circuit fault. The novel contributions include the tackling of “semi-permanent faults” and the re-usable methodology and approach for applying data analytics to any DA device data sets in order to provide diagnostic decisions and mitigate potential fault scenarios.

Comparative Analysis of Clustering Techniques in Network Traffic Faults Classification

Ubiquitous high-speed communication networks play a crucial role in the modern life, demanding the highest level of reliability and availability. Due to the rapid growth of computer networks in terms of size, complexity and heterogeneity, the probability of network faults increases. Manual network administration is hopelessly outdated; complex automated fault diagnosis and management are essential to ensure the provision and maintenance of high quality service in computer networks. Guaranteed Service with higher levels of reliability and availability for real-time applications can be achieved with a systematic approach for real-time classification of network faults, which helps in well-informed (often-automated) decision making. In this paper we discuss three different data mining algorithms as part of the proposed solution for network fault classification: K-Means, Fuzzy C Means, and Expectation Maximization. The proposed approach can help capture abnormal behavior in communication networks, thus paving the way for real-time fault classification and management. We used datasets obtained from a network with heavy and light traffic scenarios in the router and server and built a prototype to demonstrate the network traffic fault classification under given scenarios. Our empirical results reveal that the FCM is more accurate while causing computational overhead. The other two algorithms attain almost the same performance.