1# Library to Find Best eBook for Read
Author : Jun Wang
Release : 2021-09-13
Genre : Technology & Engineering
Kind : eBook
Book Rating : 990/5 ( reviews)
Book Synopsis Nuclear Power Plant Equipment Prognostics and Health Management Based on Data-driven methods by : Jun Wang
Download or read book Nuclear Power Plant Equipment Prognostics and Health Management Based on Data-driven methods written by Jun Wang. This book was released on 2021-09-13. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Stephen Ekwaro-Osire
Release : 2017-04-25
Genre : Technology & Engineering
Kind : eBook
Book Rating : 528/5 ( reviews)
Book Synopsis Probabilistic Prognostics and Health Management of Energy Systems by : Stephen Ekwaro-Osire
Download or read book Probabilistic Prognostics and Health Management of Energy Systems written by Stephen Ekwaro-Osire. This book was released on 2017-04-25. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes the formulation of an efficient methodology that estimates energy system uncertainty and predicts Remaining Useful Life (RUL) accurately with significantly reduced RUL prediction uncertainty. Renewable and non-renewable sources of energy are being used to supply the demands of societies worldwide. These sources are mainly thermo-chemo-electro-mechanical systems that are subject to uncertainty in future loading conditions, material properties, process noise, and other design parameters.It book informs the reader of existing and new ideas that will be implemented in RUL prediction of energy systems in the future. The book provides case studies, illustrations, graphs, and charts. Its chapters consider engineering, reliability, prognostics and health management, probabilistic multibody dynamical analysis, peridynamic and finite-element modelling, computer science, and mathematics.
Author : Nam-Ho Kim
Release : 2016-10-24
Genre : Technology & Engineering
Kind : eBook
Book Rating : 424/5 ( reviews)
Book Synopsis Prognostics and Health Management of Engineering Systems by : Nam-Ho Kim
Download or read book Prognostics and Health Management of Engineering Systems written by Nam-Ho Kim. This book was released on 2016-10-24. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the methods for predicting the future behavior of a system’s health and the remaining useful life to determine an appropriate maintenance schedule. The authors introduce the history, industrial applications, algorithms, and benefits and challenges of PHM (Prognostics and Health Management) to help readers understand this highly interdisciplinary engineering approach that incorporates sensing technologies, physics of failure, machine learning, modern statistics, and reliability engineering. It is ideal for beginners because it introduces various prognostics algorithms and explains their attributes, pros and cons in terms of model definition, model parameter estimation, and ability to handle noise and bias in data, allowing readers to select the appropriate methods for their fields of application.Among the many topics discussed in-depth are:• Prognostics tutorials using least-squares• Bayesian inference and parameter estimation• Physics-based prognostics algorithms including nonlinear least squares, Bayesian method, and particle filter• Data-driven prognostics algorithms including Gaussian process regression and neural network• Comparison of different prognostics algorithms divThe authors also present several applications of prognostics in practical engineering systems, including wear in a revolute joint, fatigue crack growth in a panel, prognostics using accelerated life test data, fatigue damage in bearings, and more. Prognostics tutorials with a Matlab code using simple examples are provided, along with a companion website that presents Matlab programs for different algorithms as well as measurement data. Each chapter contains a comprehensive set of exercise problems, some of which require Matlab programs, making this an ideal book for graduate students in mechanical, civil, aerospace, electrical, and industrial engineering and engineering mechanics, as well as researchers and maintenance engineers in the above fields.
Author : Olga Dolinina
Release : 2023-04-24
Genre : Technology & Engineering
Kind : eBook
Book Rating : 38X/5 ( reviews)
Book Synopsis Artificial Intelligence in Models, Methods and Applications by : Olga Dolinina
Download or read book Artificial Intelligence in Models, Methods and Applications written by Olga Dolinina. This book was released on 2023-04-24. Available in PDF, EPUB and Kindle. Book excerpt: This book is based on the accepted research papers presented in the International Conference "Artificial Intelligence in Engineering & Science" (AIES-2022). The aim of the AIES Conference is to bring together researchers involved in the theory of computational intelligence, knowledge engineering, fuzzy systems, soft computing, machine learning and related areas and applications in engineering, bioinformatics, industry, medicine, energy, smart city, social spheres and other areas. This book presents new perspective research results: models, methods, algorithms and applications in the field of Artificial Intelligence (AI). Particular emphasis is given to the medical applications - medical images recognition, development of the expert systems which could be interesting for the AI researchers as well for the physicians looking for the new ideas in medicine. The central audience of the book are researchers, industrial practitioners, students specialized in the Artificial Intelligence.
Author : Wenqing Yao
Release : 2019
Genre :
Kind : eBook
Book Rating : /5 ( reviews)
Book Synopsis Data-driven Sensor Recalibration and Fault Diagnosis in Nuclear Power Plants by : Wenqing Yao
Download or read book Data-driven Sensor Recalibration and Fault Diagnosis in Nuclear Power Plants written by Wenqing Yao. This book was released on 2019. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation explores techniques for online monitoring of nuclear power plants, especially pressurized water reactor (PWR) plants, which must have the capabilities to examine and diagnose the health of instrumentation and component, recalibrate faulty sensor measurements, and send maintenance request to the control room. Such techniques will enhance the functionality and reliability of the control and monitoring system and reduce the instrumentation maintenance labor requirement and cost.Two data-driven methods are introduced for sensor recalibration. The first method is recursive adaptive filtering that estimates the plant state parameters from a set of redundant sensor measurements. It corrects the redundant measurements based on the principle of best linear least-squares estimation and also detects and isolates anomalous measurements by adjusting their weights, in real time, based on a sequential log likelihood ratio test of sensor data. The second method is autoregressive support vector regression that is a virtual sensing technique; it predicts unknown measurements without the sensor redundancy. A support vector machine is built by learning from historical time series measurements and uses measurements from other sensors from previous time instants to estimate the current unknown. The feasibility of both approaches is validated with simulation and experimental data for PWR applications.From these perspectives, an online monitoring scheme is proposed to expand the monitoring capabilities for prognosis of sensor and component degradation. A symbolic dynamics modeling method is used to extract statistical features of time series data at the fast time scale and detect sensor and component degradation when the measurements have not shown observable anomalies at a slow time scale. The extracted features have been shown to produce distinguishable patterns between normal and faulty temperature sensor measurements. This dissertation contains detailed descriptions of the proposed algorithms, theoretical evaluations, pertinent results, and an outlook of how the research will be applied in real plants.
