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Applications Of Machine Learning In Diagnostics And Prognostics Of Wind Turbine High Speed Generator Failure
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Book Synopsis Applications of Machine Learning in Diagnostics and Prognostics of Wind Turbine High Speed Generator Failure by : Alan Turnbull
Download or read book Applications of Machine Learning in Diagnostics and Prognostics of Wind Turbine High Speed Generator Failure written by Alan Turnbull and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Data-driven Detection and Diagnosis of Faults in Traction Systems of High-speed Trains by : Hongtian Chen
Download or read book Data-driven Detection and Diagnosis of Faults in Traction Systems of High-speed Trains written by Hongtian Chen and published by Springer Nature. This book was released on 2020-04-25 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the needs of researchers and practitioners in the field of high-speed trains, especially those whose work involves safety and reliability issues in traction systems. It will appeal to researchers and graduate students at institutions of higher learning, research labs, and in the industrial R&D sector, catering to a readership from a broad range of disciplines including intelligent transportation, electrical engineering, mechanical engineering, chemical engineering, the biological sciences and engineering, economics, ecology, and the mathematical sciences.
Book Synopsis Data Science for Wind Energy by : Yu Ding
Download or read book Data Science for Wind Energy written by Yu Ding and published by CRC Press. This book was released on 2019-06-04 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science for Wind Energy provides an in-depth discussion on how data science methods can improve decision making for wind energy applications, near-ground wind field analysis and forecast, turbine power curve fitting and performance analysis, turbine reliability assessment, and maintenance optimization for wind turbines and wind farms. A broad set of data science methods covered, including time series models, spatio-temporal analysis, kernel regression, decision trees, kNN, splines, Bayesian inference, and importance sampling. More importantly, the data science methods are described in the context of wind energy applications, with specific wind energy examples and case studies. Please also visit the author’s book site at https://aml.engr.tamu.edu/book-dswe. Features Provides an integral treatment of data science methods and wind energy applications Includes specific demonstration of particular data science methods and their use in the context of addressing wind energy needs Presents real data, case studies and computer codes from wind energy research and industrial practice Covers material based on the author's ten plus years of academic research and insights
Book Synopsis Algorithms for Fault Detection and Diagnosis by : Francesco Ferracuti
Download or read book Algorithms for Fault Detection and Diagnosis written by Francesco Ferracuti and published by MDPI. This book was released on 2021-03-19 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the increasing demand for security and reliability in manufacturing and mechatronic systems, early detection and diagnosis of faults are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to increase safety, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems. The development of algorithms for health monitoring and fault and anomaly detection, capable of the early detection, isolation, or even prediction of technical component malfunctioning, is becoming more and more crucial in this context. This Special Issue is devoted to new research efforts and results concerning recent advances and challenges in the application of “Algorithms for Fault Detection and Diagnosis”, articulated over a wide range of sectors. The aim is to provide a collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions.
Book Synopsis Fault Diagnosis and Sustainable Control of Wind Turbines by : Silvio Simani
Download or read book Fault Diagnosis and Sustainable Control of Wind Turbines written by Silvio Simani and published by Butterworth-Heinemann. This book was released on 2018-01-02 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fault Diagnosis and Sustainable Control of Wind Turbines: Robust Data-Driven and Model-Based Strategies discusses the development of reliable and robust fault diagnosis and fault-tolerant (‘sustainable’) control schemes by means of data-driven and model-based approaches. These strategies are able to cope with unknown nonlinear systems and noisy measurements. The book also discusses simpler solutions relying on data-driven and model-based methodologies, which are key when on-line implementations are considered for the proposed schemes. The book targets both professional engineers working in industry and researchers in academic and scientific institutions. In order to improve the safety, reliability and efficiency of wind turbine systems, thus avoiding expensive unplanned maintenance, the accommodation of faults in their early occurrence is fundamental. To highlight the potential of the proposed methods in real applications, hardware–in–the–loop test facilities (representing realistic wind turbine systems) are considered to analyze the digital implementation of the designed solutions. The achieved results show that the developed schemes are able to maintain the desired performances, thus validating their reliability and viability in real-time implementations. Different groups of readers—ranging from industrial engineers wishing to gain insight into the applications' potential of new fault diagnosis and sustainable control methods, to the academic control community looking for new problems to tackle—will find much to learn from this work. Provides wind turbine models with varying complexity, as well as the solutions proposed and developed by the authors Addresses in detail the design, development and realistic implementation of fault diagnosis and fault tolerant control strategies for wind turbine systems Addresses the development of sustainable control solutions that, in general, do not require the introduction of further or redundant measurements Proposes active fault tolerant ('sustainable') solutions that are able to maintain the wind turbine working conditions with gracefully degraded performance before required maintenance can occur Presents full coverage of the diagnosis and fault tolerant control problem, starting from the modeling and identification and finishing with diagnosis and fault tolerant control approaches Provides MATLAB and Simulink codes for the solutions proposed
Book Synopsis An Integrated Framework of Performance Assessment and Drivetrain Prognostics for Wind Turbines by : Wenyu Zhao
Download or read book An Integrated Framework of Performance Assessment and Drivetrain Prognostics for Wind Turbines written by Wenyu Zhao and published by . This book was released on 2014 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: The growing demand for wind energy and the advancement of turbine technologies have proliferated global adoption and expansion of wind farms over the past years. Due to various causes, including logistics difficulties and lack of predictive analytics, failures and downtime occur and lead to reduced asset availability and revenue. Prognostics and health management (PHM) methodologies and techniques are considered as critical technologies, where the capability of diagnosis and prognosis for turbine degradation and failure can be considerably beneficial to prevent unexpected failures, optimize maintenance decision-making and enhance overall system performance. However, there are impeding challenges for the application of PHM techniques in wind turbine area: what is the foundation to incorporate with commonly used vibration data and activate component-level maintenance; how to apply state-of-the-art signal processing, diagnosis and prognosis techniques while wind turbine components are known to be working under dynamic operating regimes constantly; how to design a systematic approach and implement suitable algorithms on a reconfigurable platform. This thesis conducts a comprehensive review of existing data systems and analytical methods to monitor wind turbine health condition. The thesis presents a framework that integrates two most commonly used data systems in wind power area: a supervisory control and data acquisition (SCADA) system and a condition monitoring system (CMS). A system-level, overall turbine performance is assessed with purely SCADA data, whereas degradation of drivetrain components is assessed and fault detection & localization is achieved with SCADA and CMS data. The assessment of turbine performance generates a confidence value (CV) for the turbine unit, which interprets the capability to convert wind power to electrical power under varying conditions. A systematic approach is designed to pre-process data, cluster data based on a multi-regime method, Gaussian mixture models (GMM), and evaluate the cluster deviation over time. The drivetrain prognostics process combines SCADA variables with features extracted from CMS data, to evaluate the overall degradation of the drivetrain, which consists of rotor, main shaft, gearbox and generator, with a self-organizing maps (SOM) method. The minimum quantization error (MQE) metric is used for detecting drivetrain fault and locating the fault at the component-levelThe proposed framework is validated with two case studies, a 2MW onshore turbine for performance assessment and a 3MW offshore turbine for drivetrain prognosis.
Book Synopsis Safety and Reliability. Theory and Applications by : Marko Cepin
Download or read book Safety and Reliability. Theory and Applications written by Marko Cepin and published by CRC Press. This book was released on 2017-06-14 with total page 3668 pages. Available in PDF, EPUB and Kindle. Book excerpt: Safety and Reliability – Theory and Applications contains the contributions presented at the 27th European Safety and Reliability Conference (ESREL 2017, Portorož, Slovenia, June 18-22, 2017). The book covers a wide range of topics, including: • Accident and Incident modelling • Economic Analysis in Risk Management • Foundational Issues in Risk Assessment and Management • Human Factors and Human Reliability • Maintenance Modeling and Applications • Mathematical Methods in Reliability and Safety • Prognostics and System Health Management • Resilience Engineering • Risk Assessment • Risk Management • Simulation for Safety and Reliability Analysis • Structural Reliability • System Reliability, and • Uncertainty Analysis. Selected special sessions include contributions on: the Marie Skłodowska-Curie innovative training network in structural safety; risk approaches in insurance and fi nance sectors; dynamic reliability and probabilistic safety assessment; Bayesian and statistical methods, reliability data and testing; oganizational factors and safety culture; software reliability and safety; probabilistic methods applied to power systems; socio-technical-economic systems; advanced safety assessment methodologies: extended Probabilistic Safety Assessment; reliability; availability; maintainability and safety in railways: theory & practice; big data risk analysis and management, and model-based reliability and safety engineering. Safety and Reliability – Theory and Applications will be of interest to professionals and academics working in a wide range of industrial and governmental sectors including: Aeronautics and Aerospace, Automotive Engineering, Civil Engineering, Electrical and Electronic Engineering, Energy Production and Distribution, Environmental Engineering, Information Technology and Telecommunications, Critical Infrastructures, Insurance and Finance, Manufacturing, Marine Industry, Mechanical Engineering, Natural Hazards, Nuclear Engineering, Offshore Oil and Gas, Security and Protection, Transportation, and Policy Making.
Book Synopsis Artificial Intelligence Techniques for a Scalable Energy Transition by : Moamar Sayed-Mouchaweh
Download or read book Artificial Intelligence Techniques for a Scalable Energy Transition written by Moamar Sayed-Mouchaweh and published by Springer Nature. This book was released on 2020-06-19 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents research in artificial techniques using intelligence for energy transition, outlining several applications including production systems, energy production, energy distribution, energy management, renewable energy production, cyber security, industry 4.0 and internet of things etc. The book goes beyond standard application by placing a specific focus on the use of AI techniques to address the challenges related to the different applications and topics of energy transition. The contributions are classified according to the market and actor interactions (service providers, manufacturers, customers, integrators, utilities etc.), to the SG architecture model (physical layer, infrastructure layer, and business layer), to the digital twin of SG (business model, operational model, fault/transient model, and asset model), and to the application domain (demand side management, load monitoring, micro grids, energy consulting (residents, utilities), energy saving, dynamic pricing revenue management and smart meters, etc.).
Book Synopsis Wind Turbine Gearbox Diagnostics Using Artificial Intelligence by : Sofia Koukoura
Download or read book Wind Turbine Gearbox Diagnostics Using Artificial Intelligence written by Sofia Koukoura and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: To meet the latest strike price, the cost of energy from wind turbines needs to decrease.One of the biggest cost contributors to wind energy is the operation and maintenance cost. If this cost is driven down, the cost of energy of wind will substantially decrease and the reliability of the wind turbine assets need to increase. For that reason, condition monitoring systems are installed in modern wind turbines. These systems collect data and in abnormal conditions trigger alarms that are an indication of a fault. Maintenance actions can be scheduled accordingly that way, and faulty components can be replaced before catastrophic failures and large downtimes occur.Therefore, the aim of this thesis is to utilise vibration and performance data collected from wind turbine gearboxes, in order to perform fault detection and diagnosis.The data is collected at various times prior to gearbox component failures and advanced signal processing techniques are applied to reveal fault signatures. Machine learning models are trained based on features extracted from vibration spectra and operational data separately, but also a combination of these two types of data is investigated. The output is fault detection and isolation on gearbox component level. Both unit-specific and fleet-based methods are examines. The models are trained on specific turbines,but the generalization to other turbines is also examined.The above will provide a exible but robust framework for the early detection of emerging wind turbine faults. This will lead to minimisation of the wind turbine downtime and increase of the wind turbines reliability and income through operational enhancement.
Book Synopsis Current-based Fault Diagnosis and Prognosis for Wind Turbine Drivetrain Gearboxes by : Fangzhou Cheng
Download or read book Current-based Fault Diagnosis and Prognosis for Wind Turbine Drivetrain Gearboxes written by Fangzhou Cheng and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, current-based fault diagnosis methods are proposed for wind turbine drivetrain gearboxes. New signal conditioning methods are developed to extract fault features from current signals of doubly-fed induction generator (DFIG)-based wind turbines under nonstationary conditions. A deep classifier consists of stacked autoencoder and support vector machine (SVM) is proposed to improve the accuracy of fault classification results of the traditional SVM classifier. Moreover, a new fault prognosis and RUL prediction method for drivetrain gearboxes based on adaptive neuro fuzzy inference system (ANFIS) and particle filtering (PF) technique is proposed. Finally, an enhanced PF algorithm is proposed to improve the fault prognosis and RUL prediction accuracy of the ANFIS-PF method. The proposed methods have been validated by small-scale permanent magnet synchronous generator (PMSG)- and DFIG-based wind turbine drivetrain simulators in the lab and MW-scale DFIG-based wind turbines in the field.
Book Synopsis Condition Monitoring with Vibration Signals by : Hosameldin Ahmed
Download or read book Condition Monitoring with Vibration Signals written by Hosameldin Ahmed and published by John Wiley & Sons. This book was released on 2020-01-07 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an extensive, up-to-date treatment of techniques used for machine condition monitoring Clear and concise throughout, this accessible book is the first to be wholly devoted to the field of condition monitoring for rotating machines using vibration signals. It covers various feature extraction, feature selection, and classification methods as well as their applications to machine vibration datasets. It also presents new methods including machine learning and compressive sampling, which help to improve safety, reliability, and performance. Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines starts by introducing readers to Vibration Analysis Techniques and Machine Condition Monitoring (MCM). It then offers readers sections covering: Rotating Machine Condition Monitoring using Learning Algorithms; Classification Algorithms; and New Fault Diagnosis Frameworks designed for MCM. Readers will learn signal processing in the time-frequency domain, methods for linear subspace learning, and the basic principles of the learning method Artificial Neural Network (ANN). They will also discover recent trends of deep learning in the field of machine condition monitoring, new feature learning frameworks based on compressive sampling, subspace learning techniques for machine condition monitoring, and much more. Covers the fundamental as well as the state-of-the-art approaches to machine condition monitoringguiding readers from the basics of rotating machines to the generation of knowledge using vibration signals Provides new methods, including machine learning and compressive sampling, which offer significant improvements in accuracy with reduced computational costs Features learning algorithms that can be used for fault diagnosis and prognosis Includes previously and recently developed dimensionality reduction techniques and classification algorithms Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines is an excellent book for research students, postgraduate students, industrial practitioners, and researchers.
Book Synopsis Data-driven Diagnostics and Prognostics for Complex Systems by : Junchuan Shi
Download or read book Data-driven Diagnostics and Prognostics for Complex Systems written by Junchuan Shi and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in artificial intelligence or machine learning have the potential to significantly improve the effectiveness and efficiency of diagnostic and prognostic techniques. The objective of this research is to develop novel data-driven predictive models with machine learning and deep learning algorithms that allow one to model the degradation, detect the faults, as well as predict the remaining useful life (RUL) of complex systems, including bearings, gearboxes, and Lithium-ion (Li-ion) batteries. First, an enhanced ensemble learning algorithm is developed to improve the accuracy of RUL prediction by selecting diverse base learners and features at different degradation stages. The proposed method with increased diversity in base learners and features was demonstrated to be more accurate than other reported algorithms. Second, a convolutional long short-term memory (Conv-LSTM) approach is introduced to accurately classify the type, position, and direction of gear faults under different operating conditions by extracting spatiotemporal features from multiple sensors. The proposed method achieved 95% classification accuracy of fault type and 80% classification accuracy of fault location. Third, a deep learning method that combines convolutional neural networks (CNN) and bi-directional long short-term memory (BiLSTM) is developed to predict the discharge capacity and the end-of-discharge (EOD) of Li-ion batteries. The results show that by considering the discharge capacity estimated by CNN, the MAPE of EOD prediction using BiLSTM decreased from 8.52% to 3.21%. Fourth, a physics-informed machine learning method that combines the calendar and cycle aging (CCA) model and a LSTM model is developed to predict battery degradation behavior and RUL under different working conditions. The results show that the proposed method can predict the RUL of batteries accurately (10% in term of MAPE).
Book Synopsis Structural Control and Fault Detection of Wind Turbine Systems by : Hamid Reza Karimi
Download or read book Structural Control and Fault Detection of Wind Turbine Systems written by Hamid Reza Karimi and published by Energy Engineering. This book was released on 2018-05 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the rapid growth of wind energy worldwide, challenges in the operation and control of wind turbine systems are becoming increasingly important. This book conveys up to date theoretical and practical techniques applicable to the control of wind turbine systems.
Book Synopsis Application of Machine Learning and Deep Learning Methods to Power System Problems by : Morteza Nazari-Heris
Download or read book Application of Machine Learning and Deep Learning Methods to Power System Problems written by Morteza Nazari-Heris and published by Springer. This book was released on 2021-11-13 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction, classification, clustering, and fault/event detection in power systems, providing effective and promising solutions for many novel challenges faced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses.
Book Synopsis Non-Destructive Testing and Condition Monitoring Techniques in Wind Energy by : Fausto Pedro Garcia Marquez
Download or read book Non-Destructive Testing and Condition Monitoring Techniques in Wind Energy written by Fausto Pedro Garcia Marquez and published by Elsevier. This book was released on 2023-06-24 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt: Non-Destructive Testing and Condition Monitoring Techniques in Wind Energy looks at the complex and critical components of energy assets and the importance of inspection and maintenance to ensure their high availability and uninterrupted operation. Presenting the main concepts, state-of-the-art advances and case studies, this book approaches the topic by considering it as an integral part of the overall operation of any wind energy project. Linking the essential NDT subject with its sub disciplines, the book uses computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques to support analysis of prognostic problems with defined constraints and requirements. This book is the first of its kind and will provide useful insights to industrial engineers and scientists, academics and students in the possibilities that NDT and condition monitoring technologies can offer. Presents advances in Non-Destructive Techniques and Condition Monitoring Systems applied in the energy industry Provides case studies in Fault Detection and Diagnosis and Prognosis for critical variability Offers technical maintenance actions for the observation and analyses of inspection, monitoring, testing, diagnosis, prognosis and active maintenance actions in wind
Book Synopsis Preventive Maintenance and Fault Detection for Wind Turbine Generators Using a Statistical Model by : Ian Kuiler
Download or read book Preventive Maintenance and Fault Detection for Wind Turbine Generators Using a Statistical Model written by Ian Kuiler and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Vigilant fault diagnosis and preventive maintenance has the potential to significantly decrease costs associated with wind generators. As wind energy continues the upward growth in technology and continued worldwide adoption and implementation, the application of fault diagnosis techniques will become more imperative. Fault diagnosis and preventive maintenance techniques for wind turbine generators are still at an early stage compared to matured strategies used for generators in conventional power plants. The cost of wind energy can be further reduced if failures are predicted in advance of a major structural failure, which leads to less unplanned maintenance. High maintenance cost of wind turbines means that predictive strategies like fault diagnosis and preventive maintenance techniques are necessary to manage life cycle costs of critical components. Squirrel-Cage Induction Generators (SCIG) are the prevailing generator type and are more robust and cheaper to manufacturer compared to other generator types used in wind turbines. A statistical model was developed using SCADA data to estimate the relationships between winding temperatures and other variables. Predicting faults in stator windings are challenging because the unhealthy condition rapidly evolves into a functional failure.
Book Synopsis 2021 Australian & New Zealand Control Conference (ANZCC). by :
Download or read book 2021 Australian & New Zealand Control Conference (ANZCC). written by and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
