Author: Fatih Camci
Publisher: LAP Lambert Academic Publishing
Published: 2010-01
Total Pages: 156
ISBN-13: 9783838335667
DOWNLOAD EBOOKCondition-Based Maintenance (CBM) technology increases system availability and safety while reducing costs, attributed to reduced maintenance and inventory, increased capacity, and enhanced logistics and supply chain performance. Employing effective generic process monitoring methods for abrupt failures and diagnostics and prognostics algorithms for incipient failures is an important prerequisite for widespread deployment of CBM. Diagnostics is the process of identifying, localizing and determining severity of a machine failure, whereas prognostics is the process of estimating the remaining-useful-life (RUL). This work presents methods based on support vector machines and hidden Markov models to diagnose abrupt and incipient failures and to estimate the RUL. The presented methods have the ability to handle non- stationary processes. There exist three major goals of this work: detecting abrupt failures (process monitoring), identifying the state of incipient failures (health state estimation) and estimating RUL of the machine (prognostics).
Book Synopsis Process Monitoring, Diagnostics and Prognostics in MacHining Processes by : Fatih Camci
Download or read book Process Monitoring, Diagnostics and Prognostics in MacHining Processes written by Fatih Camci and published by LAP Lambert Academic Publishing. This book was released on 2010-01 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: Condition-Based Maintenance (CBM) technology increases system availability and safety while reducing costs, attributed to reduced maintenance and inventory, increased capacity, and enhanced logistics and supply chain performance. Employing effective generic process monitoring methods for abrupt failures and diagnostics and prognostics algorithms for incipient failures is an important prerequisite for widespread deployment of CBM. Diagnostics is the process of identifying, localizing and determining severity of a machine failure, whereas prognostics is the process of estimating the remaining-useful-life (RUL). This work presents methods based on support vector machines and hidden Markov models to diagnose abrupt and incipient failures and to estimate the RUL. The presented methods have the ability to handle non- stationary processes. There exist three major goals of this work: detecting abrupt failures (process monitoring), identifying the state of incipient failures (health state estimation) and estimating RUL of the machine (prognostics).