Please use this identifier to cite or link to this item:
https://hdl.handle.net/10216/109866
Author(s): | Luís Neto João Pedro Correia dos Reis Anders L. Madsen Nicolaj Søndberg Jeppesen Frank Jensen Mohamed S. Sayed Ulrich Moser Niels Lohse |
Title: | Parameter learning algorithms for continuous model improvement using operational data |
Issue Date: | 2017-10-07 |
Abstract: | In this paper, we consider the application of object-oriented Bayesian networks to failure diagnostics in manufacturing systems and continuous model improvement based on operational data. The analysis is based on an object-oriented Bayesian network developed for failure diagnostics of a one-dimensional pick-and-place industrial robot developed by IEF-Werner GmbH.We consider four learning algorithms (batch Expectation-Maximization (EM), incremental EM, Online EM and fractional updating) for parameter updating in the object-oriented Bayesian network using a real operational dataset. Also, we evaluate the performance of the considered algorithms on a dataset generated from the model to determine which algorithm is best suited for recovering the underlying generating distribution. The object-oriented Bayesian network has been integrated into both the control software of the robot as well as into a software architecture that supports diagnostic and prognostic capabilities of devices in manufacturing systems. We evaluate the time performance of the architecture to determine the feasibility of online learning from operational data using each of the four algorithms. © Springer International Publishing AG 2017. |
URI: | https://repositorio-aberto.up.pt/handle/10216/109866 |
Source: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Document Type: | Artigo em Livro de Atas de Conferência Internacional |
Rights: | openAccess |
Appears in Collections: | FEUP - Artigo em Livro de Atas de Conferência Internacional |
Files in This Item:
File | Description | Size | Format | |
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239561.pdf | 442.38 kB | Adobe PDF | View/Open |
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