Author: Le Xu
Publisher:
ISBN: 9781109855845
Category :
Languages : en
Pages : 154
Get Book Here
Book Description
In this work, logistic regression and artificial neural network are firstly compared on their capability in fault diagnosis; then an existing fuzzy classification algorithm is extended to E-algorithm to alleviate the effect of data imbalance; afterwards, the immune system based Artificial Immune Recognition System (AIRS) algorithm is investigated for its capability in fault diagnosis using real-world data; lastly, a hybrid algorithm based on E-algorithm and AIRS is proposed to embed the rule extraction capability while performing satisfactory fault cause identification.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Power distribution systems have been significantly affected by a wide range of faultcausing events; and the current outage restoration procedure may take from tens of minutes to hours. Effective outage cause identification can help to expedite the outage restoration and consequently improve the system reliability. Most current researches are based on system modeling and measurements such as voltage and current; besides, they usually target at a single feeder or a small system due to the difficulty of modeling the large-scale, nonlinear, and time-varying distribution system. In this research, various data mining approaches including statistical methods and artificial intelligence algorithms have been investigated and applied to Duke Energy distribution outage data in order to extract the outage pattern and identify the outage cause; by this means, the additional environmental information recorded in the data can be adopted in the fault diagnosis and the analysis range can be beyond the scope of a single feeder or a small system. Also, the affect of data imperfections such as data noise, data insufficiency, especially the data imbalance issue on the performance of outage cause identification have been investigated. In this work, logistic regression and artificial neural network are firstly compared on their capability in fault diagnosis; then an existing fuzzy classification algorithm is extended to Ealgorithm to alleviate the effect of data imbalance; afterwards, the immune system based Artificial Immune Recognition System (AIRS) algorithm is investigated for its capability in fault diagnosis using real-world data; lastly, a hybrid algorithm based on E-algorithm and AIRS is proposed to embed the rule extraction capability while performing satisfactory fault cause identification.
Author: Alam, Shafiq
Publisher: IGI Global
ISBN: 1466660791
Category : Computers
Languages : en
Pages : 397
Get Book Here
Book Description
Biologically-inspired data mining has a wide variety of applications in areas such as data clustering, classification, sequential pattern mining, and information extraction in healthcare and bioinformatics. Over the past decade, research materials in this area have dramatically increased, providing clear evidence of the popularity of these techniques. Biologically-Inspired Techniques for Knowledge Discovery and Data Mining exemplifies prestigious research and shares the practices that have allowed these areas to grow and flourish. This essential reference publication highlights contemporary findings in the area of biologically-inspired techniques in data mining domains and their implementation in real-life problems. Providing quality work from established researchers, this publication serves to extend existing knowledge within the research communities of data mining and knowledge discovery, as well as for academicians and students in the field.
Author: Ruqiang Yan
Publisher: CRC Press
ISBN: 1040026591
Category : Computers
Languages : en
Pages : 217
Get Book Here
Book Description
The book aims to highlight the potential of deep learning (DL)-enabled methods in intelligent fault diagnosis (IFD), along with their benefits and contributions. The authors first introduce basic applications of DL-enabled IFD, including auto-encoders, deep belief networks, and convolutional neural networks. Advanced topics of DL-enabled IFD are also explored, such as data augmentation, multi-sensor fusion, unsupervised deep transfer learning, neural architecture search, self-supervised learning, and reinforcement learning. Aiming to revolutionize the nature of IFD, Deep Neural Networks-Enabled Intelligent Fault Diangosis of Mechanical Systems contributes to improved efficiency, safety, and reliability of mechanical systems in various industrial domains. The book will appeal to academic researchers, practitioners, and students in the fields of intelligent fault diagnosis, prognostics and health management, and deep learning.
Author: Adrian-Horia Dediu
Publisher: Springer
ISBN: 3642450083
Category : Computers
Languages : en
Pages : 250
Get Book Here
Book Description
This book constitutes the refereed proceedings of the Second International Conference, TPNC 2013, held in Cáceres, Spain, in December 2013. The 19 revised full papers presented together with one invited talk were carefully reviewed and selected from 47 submissions. The papers are organized in topical sections on nature-inspired models of computation; synthesizing nature by means of computation; nature-inspired materials and information processing in nature.
Author: Jagdish C. Bansal
Publisher: Springer Science & Business Media
ISBN: 8132210417
Category : Technology & Engineering
Languages : en
Pages : 540
Get Book Here
Book Description
The book is a collection of high quality peer reviewed research papers presented in Seventh International Conference on Bio-Inspired Computing (BIC-TA 2012) held at ABV-IIITM Gwalior, India. These research papers provide the latest developments in the broad area of "Computational Intelligence". The book discusses wide variety of industrial, engineering and scientific applications of nature/bio-inspired computing and presents invited papers from the inventors/originators of novel computational techniques.
Author: Cheng He
Publisher: Springer
ISBN: 9811071799
Category : Computers
Languages : en
Pages : 652
Get Book Here
Book Description
This book constitutes the proceedings of the 12th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2017, held in Harbin, China, December 2017. The 50 full papers presented were selected from 143 submissions. The papers deal with studies abstracting computing ideas such as data structures, operations with data, ways to control operations, computing models from living phenomena or biological systems such as evolution, cells, tissues, neural networks, immune systems, and ant colonies.
Author: Sunan Huang
Publisher: CRC Press
ISBN: 0429554443
Category : Psychology
Languages : en
Pages : 275
Get Book Here
Book Description
Control systems include many components, such as transducers, sensors, actuators and mechanical parts. These components are required to be operated under some specific conditions. However, due to prolonged operations or harsh operating environment, the properties of these devices may degrade to an unacceptable level, causing more regular fault occurrences. It is therefore necessary to diagnose faults and provide the fault-accommodation control which compensates for the fault of the component by substituting a configuration of redundant elements so that the system continues to operate satisfactorily. In this book, we present a result of several years of work in the area of fault diagnosis and fault-accommodation control. It aims at information estimate methods when faults occur. The book uses the model built from the plant or process, to detect and isolate failures, in contrast to traditional hardware or statistical technologies dealing with failures. It presents model-based learning and design technologies for fault detection, isolation and identification as well as fault-tolerant control. These models are also used to analyse the fault detectability and isolability conditions and discuss the stability of the closed-loop system. It is intended to report new technologies in the area of fault diagnosis, covering fault analysis and control strategies of design for various applications. The book addresses four main schemes: modelling of actuator or sensor faults; fault detection and isolation; fault identification, and fault reconfiguration (accommodation) control. It also covers application issues in the monitoring control of actuators, providing several interesting case studies for more application-oriented readers.
Author: Weihua Li
Publisher: Springer Nature
ISBN: 9819935377
Category : Technology & Engineering
Languages : en
Pages : 474
Get Book Here
Book Description
Based on AI and machine learning, this book systematically presents the theories and methods for complex electro-mechanical system fault prognosis, intelligent diagnosis, and health state assessment in modern industry. The book emphasizes feature extraction, incipient fault prediction, fault classification, and degradation assessment, which are based on supervised-, semi-supervised-, manifold-, and deep learning; machinery degradation state tracking and prognosis by phase space reconstruction; and complex electro-mechanical system reliability assessment and health maintenance based on running state info. These theories and methods are integrated with practical industrial applications, which can help the readers get into the field more smoothly and provide an important reference for their study, research, and engineering practice.
Author: George Vachtsevanos
Publisher: Wiley
ISBN: 9780471729990
Category : Technology & Engineering
Languages : en
Pages : 0
Get Book Here
Book Description
Expert guidance on theory and practice in condition-based intelligent machine fault diagnosis and failure prognosis Intelligent Fault Diagnosis and Prognosis for Engineering Systems gives a complete presentation of basic essentials of fault diagnosis and failure prognosis, and takes a look at the cutting-edge discipline of intelligent fault diagnosis and failure prognosis technologies for condition-based maintenance. It thoroughly details the interdisciplinary methods required to understand the physics of failure mechanisms in materials, structures, and rotating equipment, and also presents strategies to detect faults or incipient failures and predict the remaining useful life of failing components. Case studies are used throughout the book to illustrate enabling technologies. Intelligent Fault Diagnosis and Prognosis for Engineering Systems offers material in a holistic and integrated approach that addresses the various interdisciplinary components of the field--from electrical, mechanical, industrial, and computer engineering to business management. This invaluably helpful book: * Includes state-of-the-art algorithms, methodologies, and contributions from leading experts, including cost-benefit analysis tools and performance assessment techniques * Covers theory and practice in a way that is rooted in industry research and experience * Presents the only systematic, holistic approach to a strongly interdisciplinary topic
