@inproceedings{8f60ec25ce084947a8e93fbf103c4c11,
title = "Kalman filter based neural network methodology for predictive maintenance: A case study on steam turbine blade performance prognostics",
abstract = "Predictive maintenance involves condition monitoring, fault detection and prediction of remaining useful life or forthcoming failures. Predictive maintenance systems for steam turbine engines offer detection, classification, and prediction (or prognosis) of potential critical component failures, and ensures substantially reducing the cost of repair and replacement of defective parts, and may even result in saving lives. This paper describes a Kalman filter based neural network approach to provide performance evaluation and residual life prediction with the objectives of improving availability and implementing maintenance before failure occurs by estimating degradation severity and the proper timing for replacement. The approach has been applied to a steam turbine blade fatigue experiment testbed to illustrate the prognostic functionalities of the methodology.",
keywords = "Fatigue damage, Kalman filter, Neural network, Prognostics, Steam turbine",
author = "Jihong Yan and Min Lv and Pengxiang Wang and Meiying Wang",
year = "2006",
doi = "10.1115/IMECE2006-15805",
language = "英语",
isbn = "0791837904",
series = "American Society of Mechanical Engineers, Manufacturing Engineering Division, MED",
publisher = "American Society of Mechanical Engineers (ASME)",
booktitle = "Proceedings of 2006 ASME International Mechanical Engineering Congress and Exposition, IMECE2006 - Manufacturing",
address = "美国",
note = "2006 ASME International Mechanical Engineering Congress and Exposition, IMECE2006 ; Conference date: 05-11-2006 Through 10-11-2006",
}