Fault management

About: Fault management is a research topic. Over the lifetime, 1994 publications have been published within this topic receiving 25629 citations.

Fault-Diagnosis Systems

Abstract: Fundamentals.- Supervision and fault management of processes - tasks and terminology.- Reliability, Availability and Maintainability (RAM).- Safety, Dependability and System Integrity.- Fault-Detection Methods.- Process Models and Fault Modelling.- Signal models.- Fault detection with limit checking.- Fault detection with signal models.- Fault detection with process-identification methods.- Fault detection with parity equations.- Fault detection with state observers and state estimation.- Fault detection of control loops.- Fault detection with Principal Component Analysis (PCA).- Comparison and combination of fault-detection methods.- Fault-Diagnosis Methods.- Diagnosis procedures and problems.- Fault diagnosis with classification methods.- Fault diagnosis with inference methods.- Fault-Tolerant Systems.- Fault-tolerant design.- Fault-tolerant components and control.- Application Examples.- Fault detection and diagnosis of DC motor drives.- Fault detection and diagnosis of a centrifugal pump-pipe-system.- Fault detection and diagnosis of an automotive suspension and the tire pressures.

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Abstract: Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.

Software fault management system

Abstract: A Software Fault Management (SFM) system (10) for managing software faults in a managed mobile telecommunications network. The SFM system (10) includes an Intelligent Management Information Base (I-MIB) (36) comprising a Management Information Base (MIB) (37) and a Knowledge Base (KB) (38) having a functional model (39) of the managed network and a trouble report/known faults (TR/KF) case base (41). The SFM system also includes an intelligent multi-agent portion having a plurality of agents (23-35) which process the software faults utilizing the functional model (39) from the I-MIB (36), case-based information, and other management information. The I-MIB and the intelligent multi-agent portion are compliant with Telecommunications Management Network (TMN) principles and framework. Fault management is both proactive and reactive. The SFM system (10) is made independent of technology-specific implementations by representing the underlying switch design knowledge in a modular and changeable form which is then interpreted by the intelligent multi-agent portion. A clear separation is maintained between the generic procedural inference mechanisms and agents, and the specific and explicit models of the different network elements of a mobile telecommunications network.

An Overview on Application of Machine Learning Techniques in Optical Networks

Abstract: Today’s telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users’ behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, machine learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing, and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude this paper proposing new possible research directions.

A Survey of Fault Management in Wireless Sensor Networks

Abstract: Wireless sensor networks are resource-constrained self-organizing systems that are often deployed in inaccessible and inhospitable environments in order to collect data about some outside world phenomenon. For most sensor network applications, point-to-point reliability is not the main objective; instead, reliable event-of-interest delivery to the server needs to be guaranteed (possibly with a certain probability). The nature of communication in sensor networks is unpredictable and failure-prone, even more so than in regular wireless ad hoc networks. Therefore, it is essential to provide fault tolerant techniques for distributed sensor applications. Many recent studies in this area take drastically different approaches to addressing the fault tolerance issue in routing, transport and/or application layers. In this paper, we summarize and compare existing fault tolerant techniques to support sensor applications. We also discuss several interesting open research directions.