Fault Management Research Articles

An efficient lossy link localization approach for wireless sensor networks

Network fault management is crucial for a wireless sensor network (WSN) to maintain a normal running state because faults (e.g., link failures) often occur. The existing lossy link localization (LLL) approach usually infers the most probable failed link set first, and then gives the fault hypothesis set. However, the inferred failed link set contains many possible failures that do not actually occur. That quantity of redundant information in the inferred set can pose a high computational burden on fault hy-pothesis inference, and consequently decreases the evaluation accuracy and increases the failure localization time. To address the issue, we propose the conditional information entropy based redundancy elimination (CIERE), a redundant lossy link elimination approach, which can eliminate most redundant information while reserving the important information. Specifically, we develop a probabilistically correlated failure model that can accurately reflect the correlation between link failures and model the nonde-terministic fault propagation. Through several rounds of mathematical derivations, the LLL problem is transformed to a set-covering problem. A heuristic algorithm is proposed to deduce the failure hypothesis set. We compare the performance of the proposed approach with those of existing LLL methods in simulation and on a real WSN, and validate the efficiency and effec-tiveness of the proposed approach.

The three-dimensional model for dependability integration in cloud computing

Dependability is one of the highly crucial issues in cloud computing environments given the serious impact of failures on user experience. Cloud computing is a complex system based on virtualization and large scalability, which makes it a frequent place for failure. In order to fight against failures in a cloud, we assure dependability differently from the common way where the focus of fault management is on the Infrastructure as a Service and on the cloud provider side only. We propose a model that integrates dependability with respect to three dimensions according to The Open Group Architecture Framework principles. These dimensions involve various cloud actors (consumer, provider, and broker). They take into consideration the interdependency between the cloud service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) and the different architecture levels (contextual, design, logical, procedural, and operations). DMD proves an enhancement of dependability attributes compared to classically designed and executed cloud systems.

Failover strategy for fault tolerance in cloud computing environment

SummaryCloud fault tolerance is an important issue in cloud computing platforms and applications. In the event of an unexpected system failure or malfunction, a robust fault‐tolerant design may allow the cloud to continue functioning correctly possibly at a reduced level instead of failing completely. To ensure high availability of critical cloud services, the application execution, and hardware performance, various fault‐tolerant techniques exist for building self‐autonomous cloud systems. In comparison with current approaches, this paper proposes a more robust and reliable architecture using optimal checkpointing strategy to ensure high system availability and reduced system task service finish time. Using pass rates and virtualized mechanisms, the proposed smart failover strategy (SFS) scheme uses components such as cloud fault manager, cloud controller, cloud load balancer, and a selection mechanism, providing fault tolerance via redundancy, optimized selection, and checkpointing. In our approach, the cloud fault manager repairs faults generated before the task time deadline is reached, blocking unrecoverable faulty nodes as well as their virtual nodes. This scheme is also able to remove temporary software faults from recoverable faulty nodes, thereby making them available for future request. We argue that the proposed SFS algorithm makes the system highly fault tolerant by considering forward and backward recovery using diverse software tools. Compared with existing approaches, preliminary experiment of the SFS algorithm indicates an increase in pass rates and a consequent decrease in failure rates, showing an overall good performance in task allocations. We present these results using experimental validation tools with comparison with other techniques, laying a foundation for a fully fault‐tolerant infrastructure as a service cloud environment. Copyright © 2017 John Wiley & Sons, Ltd.

A Technical and Economic Comparison Between Traditionally Employed and Emerging Fault Level Management Solutions at Distribution Voltages

Network fault levels are reported to be increasing and this is often attributed to an increase in network interconnectivity and localised generation. When the fault level at a substation increases beyond the rated value of the installed equipment, a power utility traditionally responded by either (i) replacing the equipment with higher rated equipment or, (ii) installing series reactors or, (iii) replacing the existing transformers with high impedance transformers. Using a South African case study, this paper compares both the effectiveness and financial viability of a superconducting fault current limiter (SFCL) against the traditional three options. When one also considers the operational or ‘energy’ cost associated with all these options it was found that a high capital cost was sometimes offset by lower energy costs over a 25 year service life. Regional prices of electricity and tariffs therefore have a significant impact on a utility’s choice of fault management solutions.

New Efficient Submodule for a Modular Multilevel Converter in Multiterminal HVDC Networks

In high-voltage applications, the magnitude of total semiconductor losses (on-state and switching) determines the viability of modular-type multilevel converters. Therefore, this paper presents a new cell arrangement that aims to lower total semiconductor loss of the modular multilevel converter (MMC) to less than that of the half-bridge modular multilevel converter (HB-MMC). Additional attributes of the proposed cell are: it eliminates the protective thyristors used in conventional half-bridge cells that deviate part of the dc-fault current away from the antiparallel diode of the main switch when the converter is blocked during a dc short-circuit fault, and it can facilitate continued operation of the MMC during cell failures without the need for a mechanical bypass switch. Thus, the MMC that uses the proposed cell retains all advantages of the HB-MMC such as full modularity of the power circuit and internal fault management. The claimed attributes of the proposed cell are verified using illustrative simulations and reduced scale experimentations. Additionally, this paper provides brief and critical discussions that highlight the attributes and limitations of popular MMC control methods and different MMC cells structures proposed in the literature, considering the power electronic system perspective.

Knowledge Acquisition for Fault Management in LTE Networks

Self-healing is one of the main functionalities of Self-Organizing-Networks. Among self-healing functions, diagnosis or root cause analysis, consisting of identifying the fault cause in problematic cells, is one of the most complex tasks. Expert systems, such as Fuzzy Logic Controllers or Bayesian Networks, have been previously proposed to implement automatic diagnosis systems in the radio access segment of mobile communication networks. In order to achieve accurate results, these diagnosis systems should contain the knowledge of experienced LTE troubleshooting experts. However, these experts normally have neither the time nor the expertise in artificial intelligence to define the expert system. In this work, we propose a novel knowledge acquisition system that obtains this knowledge in the least possible intrusive way. The proposed method collects the Performance Indicators data from the relevant time intervals together with the expert’s diagnosis and uses them as inputs for a Data Mining algorithm to extract diagnosis rules.

A Taxonomy of Faults for Wireless Sensor Networks

Over the last decade, Wireless Sensor Networks (WSN) went from being a promising technology to the main enabler of countless Internet of Things applications in all types of areas. In industry, WSNs are now used for monitoring and controlling industrial processes, with the benefits of low installation costs, self-organization, self-configuration, and added functionality. Nevertheless, despite the fact that base WSN technologies are quite stable and subject to standardization, they have kept one of their main characteristics: fault-proneness. As a result, in recent years considerable effort has been made in order to provide mechanisms that increase the availability, reliability and maintainability of this type of networks. In this context, a whole range of techniques such as fault detection, fault identification and fault diagnosis used in other research fields are now being applied to WSNs. Unfortunately, this has not led to a consistent, comprehensive WSN fault taxonomy that can be used to characterize and/or classify faults. Neglecting the importance of WSN fault characterization (e.g., when using supervised algorithms for anomaly detection) may lead to bad classifiers and, consequently, bad fault handling procedures and/or tools. In this paper, we start by reviewing base fault management concepts and techniques that can be applied to WSNs. We then proceed to propose and present a comprehensive WSN fault taxonomy that can be used not only in general purpose WSNs but also in Industrial WSNs. Finally, the proposed taxonomy is validated by applying it to an extensive set of faults described in the literature.

A Survey on Fault Management in Software-Defined Networks

Software-defined networking (SDN) is an emerging paradigm that has become increasingly popular in recent years. The core idea is to separate the control and data planes, allowing the construction of network applications using high-level abstractions that are translated to network devices through a southbound interface. SDN architecture is composed of three layers: 1) infrastructure layer, responsible exclusively for data forwarding; 2) control layer, which maintains the network view and provides core network abstractions; and 3) application layer, which uses abstractions provided by the control layer to implement network applications. SDN provides features, such as flexibility and programmability, that are key enablers to meet current network requirements (e.g., multi-tenant cloud networks and elastic optical networks). However, along with its benefits, SDN also brings new issues. In this survey we focus on issues related to fault management. Different fault management threat vectors are introduced by each layer, as well as by the interface between layers. Nevertheless, besides addressing fault management issues of its architecture, SDN also must handle the same problems faced by legacy networks. However, programmability and centralized management might be used to provide flexibility to deal with those issues. This paper presents an overview of fault management in SDN. The major contributions of this paper are as follows: 1) identification of the main fault management issues in SDN and classification according to the affected layers; 2) survey of efforts that address those issues and classification according to the affected planes, issues concerned, general approaches, and features; and 3) discussion about trade-offs of different approaches and their suitability for different scenarios.

Network Self-Fault Management Based on Multi-Intelligent Agents and Windows Management Instrumentation (WMI)

This paper proposed a new method for network self-fault management (NSFM) based on two technologies: intelligent agent to automate fault management tasks, and Windows Management Instrumentations (WMI) to identify the fault faster when resources are independent (different type of devices). The proposed network self-fault management reduced the load of network traffic by reducing the request and response between the server and client, which achieves less downtime for each node in state of fault occurring in the client. The performance of the proposed system is measured by three measures: efficiency, availability, and reliability. A high efficiency average is obtained depending on the faults occurred in the system which reaches to 92.19%, availability 92.375%, and reliability 100%. The proposed system managed five devices. The NSFM implemented using Java and C# languages.

Service Provider DevOps

Although there is consensus that software defined networking and network functions virtualization overhaul service provisioning and deployment, the community still lacks a definite answer on how carrier-grade operations praxis needs to evolve. This article presents what lies beyond the first evolutionary steps in network management, identifies the challenges in service verification, observability, and troubleshooting, and explains how to address them using our Service Provider DevOps (SP-DevOps) framework. We compendiously cover the entire process from design goals to tool realization and employ an elastic version of an industry-standard use case to show how on-the-fly verification, software-defined monitoring, and automated troubleshooting of services reduce the cost of fault management actions. We assess SP-DevOps with respect to key attributes of software-defined telecommunication infrastructures both qualitatively and quantitatively, and demonstrate that SP-DevOps paves the way toward carrier-grade operations and management in the network virtualization era.

Web Crawler : Review of Different Types of Web Crawler, Its Issues, Applications and Research Opportunities

Today's search engines are equipped with dedicated agents known as “web crawlers” keen to crawling large web contents online which are analyzed and indexed and make the content available to users. Crawlers act together with thousands of web servers over periods expanding from weeks to several years. These crawlers visits several thousands of pages every second, includes a high-performance fault manager, are platform independent or dependent and are able to get used to a wide range of configurations without including additional hardware. This paper is focused on prerequisites of crawler, process of crawling and different types of crawlers. This paper give review about some potential issues related to crawler, applications and research area of web crawler. Keywords: search engine, web crawler, www, Indexing, website analysis,

Research on the High-Speed Highway Bridge Stability Analysis Model Based on Particle Swarm Optimization with Gradient Descent Algorithm

In this paper, we conduct research on high-speed highway bridge stability analysis model based on particle swarm optimization with gradient descent algorithm. Road and that the bridge construction technology management and the quality control is the key to guarantee the quality of the project construction, prevention is the focus of the construction quality common fault occurs, the scientific implementation of help enterprises to reduce the quality common fault management cost that will improve economic efficiency of the enterprises. To enhance the quality of the traditional structure, in this paper, we propose the new particle swarm optimization with the gradient descent algorithm based method for enhancement which achieves the satisfactory result.

Developing Decision Aids to Enable Human Spaceflight Autonomy

As NASA explores destinations beyond the moon, the distance between Earth and spacecraft will increase communication delays between astronauts and the Mission Control Center (MCC). Today, astronauts coordinate with MCC to request assistance and await approval to perform tasks. Many of these coordination tasks require multiple exchanges of information, (for example, taking turns). In the presence of long communication delays, the length of time between turns may lead to inefficiency or increased mission risk. Future astronauts will need software‐based decision aids to enable them to work autonomously from the Mission Control Center. These tools require the appropriate combination of mission operations functions, for example, automated planning and fault management, troubleshooting recommendations, easy‐to‐access information, and just‐in‐time training. Ensuring that these elements are properly designed and integrated requires an integrated human factors approach. This article describes a recent demonstration of autonomous mission operations using a novel software‐based decision aid on board the International Space Station. We describe how this new technology changes the way astronauts coordinate with MCC, and how the lessons learned from these early demonstrations will enable the operational autonomy needed to ensure astronauts can safely journey to Mars, and beyond.

Multifunctional Megawatt-Scale Medium Voltage DC Test Bed Based on Modular Multilevel Converter Technology

Recent developments in modular multilevel converters (MMCs) provide new opportunities for medium-voltage dc (MVDC) systems for all-electric ship design and offshore wind farms. The Center for Advanced Power Systems at Florida State University has recently commissioned a new MVDC power hardware-in-the-loop laboratory rated at 5 MW at dc voltages from 6 to 24 kV. The new lab features four individual MMCs, each composed of 36 full-bridge cells, capable of delivering 210 A at any voltage in the range of 0–6 kV. This paper describes the entire system in detail, including the advanced current and voltage control concepts along with the state-of-the-art digital control hardware. Selected commissioning results are shown, which demonstrate the performance of the system under dynamic conditions and provide comparison with simulations obtained from a corresponding controller hardware-in-the-loop setup. The results indicate that an MMC-based MVDC system is a strong candidate for the ship power system because of its excellent fault management capability. The setup can be used for the understanding and design of fast fault management schemes in a breakerless MVDC system in the future all-electric ship.

ACCRS: autonomic based cloud computing resource scaling

A cloud computing model gives cloud service providers the ability to retain multiple workloads on a single physical system. However, efficient resource provisioning and possible system fault management in the cloud can be a challenge. Early fault detection can provide room to recover from potential faults before impacting QoS. Current static techniques of fault management in computing systems are not satisfactory enough to safeguard the QoS requested by cloud users. Thus, new smart techniques are needed. This paper presents the ACCRS framework for cloud computing infrastructures to advance system’s utilization level, reduce cost and power consumption and fulfil SLAs. The ACCRS framework employs Autonomic Computing basic components which includes state monitoring, planning, decision making, fault predication, detection, and root cause analysis for recovery actions to improve system’s reliability, availability, and utilization level by scaling resources in response to changes in the cloud system state.

Microservices tenets

Some microservices proponents claim that microservices form a new architectural style; in contrast, advocates of service-oriented architecture (SOA) argue that microservices merely are an implementation approach to SOA. This overview and vision paper first reviews popular introductions to microservices to identify microservices tenets. It then compares two microservices definitions and contrasts them with SOA principles and patterns. This analysis confirms that microservices indeed can be seen as a development- and deployment-level variant of SOA; such microservices implementations have the potential to overcome the deficiencies of earlier approaches to SOA realizations by employing modern software engineering paradigms and Web technologies such as domain-driven design, RESTful HTTP, IDEAL cloud application architectures, polyglot persistence, lightweight containers, a continuous DevOps approach to service delivery, and comprehensive but lean fault management. However, these paradigms and technologies also cause a number of additional design choices to be made and create new options for many "distribution classics" type of architectural decisions. As a result, the cognitive load for (micro-)services architects increases, as well as the design, testing and maintenance efforts that are required to benefit from an adoption of microservices. To initiate and frame the buildup of architectural knowledge supporting microservices projects, this paper compiles related practitioner questions; it also derives research topics from these questions. The paper concludes with a summarizing position statement: microservices constitute one particular implementation approach to SOA (service development and deployment).

Platform for the management of hydraulic chambers based on mobile devices and Bluetooth Low-Energy motes

Agencies and institutions that are in charge of water distribution and treatment facilities devote significant resources to inspection and control of such facilities. In this paper, the procedures involved in these activities are discussed and optimized for automated treatment with mobile devices. To this purpose, a novel remote control application for the management and detection of faults and breakdowns of collective water distribution networks in irrigation systems has been developed. The developed platform makes use of BLE (Bluetooth Low-Energy) technology to provide contactless, context-and-positioning sensitive information to the user, simplifying periodic inspections and repairing tasks of the network infrastructure. Devices would be equipped with BLE-enabled motes capable of transmitting a beacon signal for equipment discovering. Operators in turn, would carry tablets or smartphones configured to detect the motes and stablish a link in case additional data must be exchanged. The tablet also allows the operator to revise and fill in a device’s form customized with context sensitive information gathered from both the mote and the enterprise cloud services and finally, to update the information in the online databases.

A Comprehensive DC Short-Circuit Fault Ride Through Strategy of Hybrid Modular Multilevel Converters (MMCs) for Overhead Line Transmission

The modular multilevel converter (MMC) is a promising candidate for voltage-sourced-converter-based high-voltage direct current (VSC-HVDC) transmission. The dc short-circuit fault management is a crucial issue especially for overhead line transmission, where the nonpermanent dc short-circuit faults occasionally occur. In this paper, a comprehensive dc short-circuit fault ride through (FRT) strategy is proposed for a hybrid MMC which combines half-bridge submodules and full-bridge submodules. By the proposed method, the hybrid MMC-based VSC-HVDC system is able to ride through a pole-to-pole short-circuit fault supporting the ac grid as a static synchronous compensator. Moreover, the transmission system can still transmit one-half of the rated active power in case of a pole-to-ground short-circuit fault without increasing power semiconductor stresses or requiring higher insulation levels of the electric equipment. The proposed method ensures successful regulation of arm currents and submodule capacitor voltages during the FRT process. Validity of the proposed strategy is verified by both computer simulation and experiment results.

CPS-based fault-tolerance method for smart factories

Abstract In this paper, we propose a fault tolerance method in smart factories which features a simulation-based framework and a fault management application, FORTUNA (Fault-tOleRanT and aUtomated decisioN making Application). The simulation-based framework is characterized by a Virtual Factory (VF) which plays an integrator role in CPS architecture for the smart factory. The VF provides two key functionalities, (1) a simulation of CPS models represented by ECML (ETRI CPS Modeling Language), and (2) a state estimation based on massive CPS data. By deploying Kalman filter, a predictor-corrector type estimator, FORTUNA not only detects faults through the realtime observation of CPS data but also predicts potential failures by the simulation. It is demonstrated that the method can work effectively in industrial systems with real dataset.

Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations.

Modern world has witnessed a dramatic increase in our ability to collect, transmit and distribute real-time monitoring and surveillance data from large-scale information systems and cyber-physical systems. Detecting system anomalies thus attracts significant amount of interest in many fields such as security, fault management, and industrial optimization. Recently, invariant network has shown to be a powerful way 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. To address these limitations, in this paper 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. Extensive experiments on synthetic datasets, bank information system datasets, and coal plant cyber-physical system datasets demonstrate the effectiveness of our approach.