Parameter_Learning_Algorithms_for_Continuous_Model_Improvement_Using_Operational_Data.pdf (457.75 kB)
Download fileParameter learning algorithms for continuous model improvement using operational data
conference contribution
posted on 2017-06-02, 13:49 authored by Anders L. Madsen, Nicolaj Sondberg-Jeppesen, Frank Jensen, Mohamed S. Sayed, Ulrich Moser, Luis Neto, Joao Reis, Niels LohseNiels LohseIn 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 on-line learning from operational data using each of the four algorithms.
Funding
This work is part of the project ”Health Monitoring and Life-Long Capability Management for SELf-SUStaining Manufacturing Systems (SelSus)” which is funded by the Commission of the European Communities under the 7th Framework Programme, Grant agreement no: 609382.
History
School
- Mechanical, Electrical and Manufacturing Engineering