On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Expert Systems with Applications: An International Journal
Machine condition prognosis based on sequential Monte Carlo method
Expert Systems with Applications: An International Journal
On-line learning algorithms for locally recurrent neural networks
IEEE Transactions on Neural Networks
Local prediction of network traffic measurements data based on relevance vector machine
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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The investigation of damage propagation mechanisms on a selected safety-critical component or structure requires the quantification of its remaining useful life (RUL) to verify until when it can continue performing the required function. In this work, a relevance vector machine (RVM), that is a Bayesian elaboration of support vector machine (SVM), automatically selects a low number of significant basis functions, called relevant vectors (RVs), for degradation model identification, degradation state regression and RUL estimation. In particular, RVM capabilities are exploited to provide estimates of the RUL of a component undergoing crack growth, within an original combination of data-driven and model-based approaches to prognostics. The application to a case study shows that the proposed approach compares well to other methods (the model-based Bayesian approach of particle filtering and the data-driven fuzzy similarity-based approach) with respect to computational demand, data requirements, accuracy and that its Bayesian setting allows representing and propagating the uncertainty in the estimates.