An adaptive probabilistic data-driven methodology for prognosis of the fatigue life of composite structures
Authors:
- Nick EleftheroglouNENick Eleftheroglou
- Dimitrios ZarouchasDZDimitrios Zarouchas
- Rinze BenedictusRBRinze Benedictus
Open Access
- 1 August 2020
- journal article
- research article
- Published by Elsevier in Composite Structures
- Vol. 245
Abstract
No abstract availableThis publication has 18 references indexed in Scilit:
- An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration processReliability Engineering & System Safety, 2014
- Model-based and data-driven prognostics under different available informationProbabilistic Engineering Mechanics, 2013
- A Monotonic Degradation Assessment Index of Rolling Bearings Using Fuzzy Support Vector Data Description and Running TimeSensors, 2012
- Detection and diagnosis of bearing and cutting tool faults using hidden Markov modelsMechanical Systems and Signal Processing, 2011
- Prognostic modelling options for remaining useful life estimation by industryMechanical Systems and Signal Processing, 2011
- A model for residual life prediction based on Brownian motion with an adaptive driftMicroelectronics Reliability, 2011
- A prognosis method using age-dependent hidden semi-Markov model for equipment health predictionMechanical Systems and Signal Processing, 2011
- A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodologyMechanical Systems and Signal Processing, 2007
- HMMs for diagnostics and prognostics in machining processesInternational Journal of Production Research, 2005
- A tutorial on hidden Markov models and selected applications in speech recognitionProceedings of the IEEE, 1989