1# Library to Find Best eBook for Read
Author : Mohammed Lskaafi
Release : 2016-03-08
Genre :
Kind : eBook
Book Rating : 860/5 ( reviews)
Book Synopsis Fault Diagnosis and Failure Prognostics of Lithium-ion Battery by : Mohammed Lskaafi
Download or read book Fault Diagnosis and Failure Prognostics of Lithium-ion Battery written by Mohammed Lskaafi. This book was released on 2016-03-08. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Mohammed Ali Lskaafi
Release : 2015
Genre : Failure analysis (Engineering)
Kind : eBook
Book Rating : /5 ( reviews)
Book Synopsis Fault Diagnosis and Failure Prognostics of Lithium-ion Battery Based on Least Squares Support Vector Machine and Memory Particle Filter Framework by : Mohammed Ali Lskaafi
Download or read book Fault Diagnosis and Failure Prognostics of Lithium-ion Battery Based on Least Squares Support Vector Machine and Memory Particle Filter Framework written by Mohammed Ali Lskaafi. This book was released on 2015. Available in PDF, EPUB and Kindle. Book excerpt: A novel data driven approach is developed for fault diagnosis and remaining useful life (RUL) prognostics for lithium-ion batteries using Least Square Support Vector Machine (LS-SVM) and Memory-Particle Filter (M-PF). Unlike traditional data-driven models for capacity fault diagnosis and failure prognosis, which require multidimensional physical characteristics, the proposed algorithm uses only two variables: Energy Efficiency (EE), and Work Temperature. The aim of this novel framework is to improve the accuracy of incipient and abrupt faults diagnosis and failure prognosis. First, the LSSVM is used to generate residual signal based on capacity fade trends of the Li-ion batteries. Second, adaptive threshold model is developed based on several factors including input, output model error, disturbance, and drift parameter. The adaptive threshold is used to tackle the shortcoming of a fixed threshold. Third, the M-PF is proposed as the new method for failure prognostic to determine Remaining Useful Life (RUL). The M-PF is based on the assumption of the availability of real-time observation and historical data, where the historical failure data can be used instead of the physical failure model within the particle filter. The feasibility of the framework is validated using Li-ion battery prognostic data obtained from the National Aeronautic and Space Administration (NASA) Ames Prognostic Center of Excellence (PCoE). The experimental results show the following: (1) fewer data dimensions for the input data are required compared to traditional empirical models; (2) the proposed diagnostic approach provides an effective way of diagnosing Li-ion battery fault; (3) the proposed prognostic approach can predict the RUL of Li-ion batteries with small error, and has high prediction accuracy; and, (4) the proposed prognostic approach shows that historical failure data can be used instead of a physical failure model in the particle filter.
Author : Hamid Reza Karimi
Release : 2021-06-05
Genre : Technology & Engineering
Kind : eBook
Book Rating : 886/5 ( reviews)
Book Synopsis Fault Diagnosis and Prognosis Techniques for Complex Engineering Systems by : Hamid Reza Karimi
Download or read book Fault Diagnosis and Prognosis Techniques for Complex Engineering Systems written by Hamid Reza Karimi. This book was released on 2021-06-05. Available in PDF, EPUB and Kindle. Book excerpt: Fault Diagnosis and Prognosis Techniques for Complex Engineering Systems gives a systematic description of the many facets of envisaging, designing, implementing, and experimentally exploring emerging trends in fault diagnosis and failure prognosis in mechanical, electrical, hydraulic and biomedical systems. The book is devoted to the development of mathematical methodologies for fault diagnosis and isolation, fault tolerant control, and failure prognosis problems of engineering systems. Sections present new techniques in reliability modeling, reliability analysis, reliability design, fault and failure detection, signal processing, and fault tolerant control of engineering systems. Sections focus on the development of mathematical methodologies for diagnosis and prognosis of faults or failures, providing a unified platform for understanding and applicability of advanced diagnosis and prognosis methodologies for improving reliability purposes in both theory and practice, such as vehicles, manufacturing systems, circuits, flights, biomedical systems. This book will be a valuable resource for different groups of readers – mechanical engineers working on vehicle systems, electrical engineers working on rotary machinery systems, control engineers working on fault detection systems, mathematicians and physician working on complex dynamics, and many more. Presents recent advances of theory, technological aspects, and applications of advanced diagnosis and prognosis methodologies in engineering applications Provides a series of the latest results, including fault detection, isolation, fault tolerant control, failure prognosis of components, and more Gives numerical and simulation results in each chapter to reflect engineering practices
Author : Amardeep Singh Sidhu
Release : 2013
Genre : Electric circuit analysis
Kind : eBook
Book Rating : /5 ( reviews)
Book Synopsis Fault Diagnosis of Lithium Ion Battery Using Multiple Model Adaptive Estimation by : Amardeep Singh Sidhu
Download or read book Fault Diagnosis of Lithium Ion Battery Using Multiple Model Adaptive Estimation written by Amardeep Singh Sidhu. This book was released on 2013. Available in PDF, EPUB and Kindle. Book excerpt: Lithium ion (Li-ion) batteries have become integral parts of our lives; they are widely used in applications like handheld consumer products, automotive systems, and power tools among others. To extract maximum output from a Li-ion battery under optimal conditions it is imperative to have access to the state of the battery under every operating condition. Faults occurring in the battery when left unchecked can lead to irreversible, and under extreme conditions, catastrophic damage. In this thesis, an adaptive fault diagnosis technique is developed for Li-ion batteries. For the purpose of fault diagnosis the battery is modeled by using lumped electrical elements under the equivalent circuit paradigm. The model takes into account much of the electro-chemical phenomenon while keeping the computational effort at the minimum. The diagnosis process consists of multiple models representing the various conditions of the battery. A bank of observers is used to estimate the output of each model; the estimated output is compared with the measurement for generating residual signals. These residuals are then used in the multiple model adaptive estimation (MMAE) technique for generating probabilities and for detecting the signature faults. The effectiveness of the fault detection and identification process is also dependent on the model uncertainties caused by the battery modeling process. The diagnosis performance is compared for both the linear and nonlinear battery models. The non-linear battery model better captures the actual system dynamics and results in considerable improvement and hence robust battery fault diagnosis in real time. Furthermore, it is shown that the non-linear battery model enables precise battery condition monitoring in different degrees of over-discharge.
Author : Michael G. Pecht
Release : 2018-08-15
Genre : Technology & Engineering
Kind : eBook
Book Rating : 300/5 ( reviews)
Book Synopsis Prognostics and Health Management of Electronics by : Michael G. Pecht
Download or read book Prognostics and Health Management of Electronics written by Michael G. Pecht. This book was released on 2018-08-15. Available in PDF, EPUB and Kindle. Book excerpt: An indispensable guide for engineers and data scientists in design, testing, operation, manufacturing, and maintenance A road map to the current challenges and available opportunities for the research and development of Prognostics and Health Management (PHM), this important work covers all areas of electronics and explains how to: assess methods for damage estimation of components and systems due to field loading conditions assess the cost and benefits of prognostic implementations develop novel methods for in situ monitoring of products and systems in actual life-cycle conditions enable condition-based (predictive) maintenance increase system availability through an extension of maintenance cycles and/or timely repair actions; obtain knowledge of load history for future design, qualification, and root cause analysis reduce the occurrence of no fault found (NFF) subtract life-cycle costs of equipment from reduction in inspection costs, downtime, and inventory Prognostics and Health Management of Electronics also explains how to understand statistical techniques and machine learning methods used for diagnostics and prognostics. Using this valuable resource, electrical engineers, data scientists, and design engineers will be able to fully grasp the synergy between IoT, machine learning, and risk assessment.
