Diagnóstico de fallas en computación móvil usando TwinSVM

Introduction: Mobile computing systems (MCS) comes up with the challenge of low communication bandwidth and energy due to the mobile nature of the network. These features sometimes may come up with the undesirable behaviour of the system that eventually affects the efficiency of the system. Problem: Fault tolerance in MCS will increase the efficiency of the system even in the presence of faults. Objective: The main objective of this work is the development of the Monitoring Framework and Fault Detection and Classification. Methodology: For the Node Monitoring and for the detection and classification of faults in the system a neighbourhood comparison-based technique has been proposed. The proposed framework uses Twin Support Vector Machine (TWSVM) algorithm has been applied to build classifier for fault classification in the mobile network. Results: The proposed system has been compared with the existing techniques and has been evaluated towards calculating the detection accuracy, latency, energy consumption, packet delivery ratio, false classification rate and false positive rate. Conclusion: The proposed framework performs better in terms of all the selected parameters.Introducción: este artículo es el resultado de la investigación “Diagnóstico de fallas en la computación móvil usando TwinSVM” desarrollada en la Universidad Técnica I.K Gujral Punjab en Punjab, India en 2021.Problema: dado que los recursos en los sistemas informáticos móviles son limitados y un sistema tiene un ancho de banda, energía y movilidad de nodos limitados, el comportamiento deseado de la red puede cambiar si hay fallas.Objetivo: para lograr la tolerancia a fallas, de modo que un sistema móvil pueda operar incluso en presencia de fallas, se implementó un enfoque de dos temporizadores en el marco de detección, que luego se mejoró y perfeccionó con el uso del clasificador TwinSVM. Este clasificador ayuda a identificar nodos atípicos, lo que hace que el enfoque sea más tolerante a fallas.Metodología: el marco de monitoreo clasifica el nodo detectado como normal, defectuoso o parcialmente de-fectuoso, iniciando un temporizador de verificación de latidos y otro temporizador de verificación de relevancia en caso de que el nodo no responda al primer temporizador, que se prueba más usando TwinSVM, que mejora su eficiencia mediante la detección de valores atípicos.Resultados: el marco propuesto funciona mejor en términos de precisión de detección, consumo de energía, latencia y relación de caída de paquetes, todos los cuales han sido mejorados.Conclusión: el diagnóstico de fallas que utiliza el clasificador de aprendizaje automático TwinSVM funciona mejor en términos de falsas alarmas y tasas de falsos positivos y es adecuado para proporcionar tolerancia a fallas en sistemas informáticos móviles.Originalidad: a través de esta investigación, se ha desarrollado una versión única de detección de fallas en computación móvil utilizando un enfoque basado en clasificadores.Limitaciones: la falta de otras técnicas de detección de fallas cae dentro de la clasificación de fallas

Probabilistic Modeling of Process Systems with Application to Risk Assessment and Fault Detection

Three new methods of joint probability estimation (modeling), a maximum-likelihood maximum-entropy method, a constrained maximum-entropy method, and a copula-based method called the rolling pin (RP) method, were developed. Compared to many existing probabilistic modeling methods such as Bayesian networks and copulas, the developed methods yield models that have better performance in terms of flexibility, interpretability and computational tractability. These methods can be used readily to model process systems and perform risk analysis and fault detection at steady state conditions, and can be coupled with appropriate mathematical tools to develop dynamic probabilistic models. Also, a method of performing probabilistic inference using RP-estimated joint probability distributions was introduced; this method is superior to Bayesian networks in several aspects. The RP method was also applied successfully to identify regression models that have high level of flexibility and are appealing in terms of computational costs.Ph.D., Chemical Engineering -- Drexel University, 201

Condition monitoring systems : a systematic literature review on machine-learning methods improving offshore-wind turbine operational management

Information is key. Offshore wind farms are installed with supervisory control and data acquisition systems (SCADA) gathering valuable information. Determining the precise condition of an asset is essential on achieving the expected operational lifetime and efficiency. Equipment fault detection is necessary to achieve this. This paper presents a systematic literature review of machine learning methods applied to condition monitoring systems, using both vibration information and SCADA data together. Starting with conventional methods using vibration models, such as Fast-Fourier transforms to five prominent supervised learning regression models; Artificial neural network, support vector regression, Bayesian network, random forest and K-nearest neighbour. This review specifically looks at how conventional vibration data can be combined with SCADA data to determine the assets condition

Proceedings - 30. Workshop Computational Intelligence : Berlin, 26. - 27. November 2020

Dieser Tagungsband enthält die Beiträge des 30. Workshops Computational Intelligence. Die Schwerpunkte sind Methoden, Anwendungen und Tools für Fuzzy-Systeme, Künstliche Neuronale Netze, Evolutionäre Algorithmen und Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen und Benchmark-Problemen

Establishment of a novel predictive reliability assessment strategy for ship machinery

There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme.There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme

Advanced methodologies for reliability-based design optimization and structural health prognostics

Failures of engineered systems can lead to significant economic and societal losses. To minimize the losses, reliability must be ensured throughout the system's lifecycle in the presence of manufacturing variability and uncertain operational conditions. Many reliability-based design optimization (RBDO) techniques have been developed to ensure high reliability of engineered system design under manufacturing variability. Schedule-based maintenance, although expensive, has been a popular method to maintain highly reliable engineered systems under uncertain operational conditions. However, so far there is no cost-effective and systematic approach to ensure high reliability of engineered systems throughout their lifecycles while accounting for both the manufacturing variability and uncertain operational conditions. Inspired by an intrinsic ability of systems in ecology, economics, and other fields that is able to proactively adjust their functioning to avoid potential system failures, this dissertation attempts to adaptively manage engineered system reliability during its lifecycle by advancing two essential and co-related research areas: system RBDO and prognostics and health management (PHM). System RBDO ensures high reliability of an engineered system in the early design stage, whereas capitalizing on PHM technology enables the system to proactively avoid failures in its operation stage. Extensive literature reviews in these areas have identified four key research issues: (1) how system failure modes and their interactions can be analyzed in a statistical sense; (2) how limited data for input manufacturing variability can be used for RBDO; (3) how sensor networks can be designed to effectively monitor system health degradation under highly uncertain operational conditions; and (4) how accurate and timely remaining useful lives of systems can be predicted under highly uncertain operational conditions. To properly address these key research issues, this dissertation lays out four research thrusts in the following chapters: Chapter 3 - Complementary Intersection Method for System Reliability Analysis, Chapter 4 - Bayesian Approach to RBDO, Chapter 5 - Sensing Function Design for Structural Health Prognostics, and Chapter 6 - A Generic Framework for Structural Health Prognostics. Multiple engineering case studies are presented to demonstrate the feasibility and effectiveness of the proposed RBDO and PHM techniques for ensuring and improving the reliability of engineered systems within their lifecycles

Proceedings - 30. Workshop Computational Intelligence : Berlin, 26. - 27. November 2020

The proceedings of the 30th workshop on computational intelligence focus on methods, applications, and tools for fuzzy systems, artificial neural networks, deep learning, system identification, and data mining techniques

Variable selection for wind turbine condition monitoring and fault detection system

With the fast growth in wind energy, the performance and reliability of the wind power generation system has become a major issue in order to achieve cost-effective generation. Integration of condition monitoring system (CMS) in the wind turbine has been considered as the most viable solution, which enhances maintenance scheduling and achieving a more reliable system. However, for an effective CMS, large number of sensors and high sampling frequency are required, resulting in a large amount of data to be generated. This has become a burden for the CMS and the fault detection system. This thesis focuses on the development of variable selection algorithm, such that the dimensionality of the monitoring data can be reduced, while useful information in relation to the later fault diagnosis and prognosis is preserved. The research started with a background and review of the current status of CMS in wind energy. Then, simulation of the wind turbine systems is carried out in order to generate useful monitoring data, including both healthy and faulty conditions. Variable selection algorithms based on multivariate principal component analysis are proposed at the system level. The proposed method is then further extended by introducing additional criterion during the selection process, where the retained variables are targeted to a specific fault. Further analyses of the retained variables are carried out, and it has shown that fault features are present in the dataset with reduced dimensionality. Two detection algorithms are then proposed utilising the datasets obtained from the selection algorithm. The algorithms allow accurate detection, identification and severity estimation of anomalies from simulation data and supervisory control and data acquisition data from an operational wind farm. Finally an experimental wind turbine test rig is designed and constructed. Experimental monitoring data under healthy and faulty conditions is obtained to further validate the proposed detection algorithms

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201