Journal Published Online: 02 October 2018
Volume 2, Issue 1

Predictive Model Markup Language (PMML) Representation of Bayesian Networks: An Application in Manufacturing

CODEN: SSMSCY

Abstract

Bayesian networks (BNs) represent a promising approach for the aggregation of multiple uncertainty sources in manufacturing networks and other engineering systems for the purposes of uncertainty quantification, risk analysis, and quality control. A standardized representation for BN models will aid in their communication and exchange across the web. This article presents an extension to the predictive model markup language (PMML) standard for the representation of a BN, which may consist of discrete variables, continuous variables, or their combination. The PMML standard is based on extensible markup language (XML) and used for the representation of analytical models. The BN PMML representation is available in PMML v4.3 released by the Data Mining Group. We demonstrate the conversion of analytical models into the BN PMML representation, and the PMML representation of such models into analytical models, through a Python parser. The BNs obtained after parsing PMML representation can then be used to perform Bayesian inference. Finally, we illustrate the developed BN PMML schema for a welding process.

Author Information

Nannapaneni, Saideep
Department of Industrial, Systems, and Manufacturing Engineering, Wichita State University, Wichita, KS, USA
Narayanan, Anantha
Department of Mechanical Engineering, University of Maryland, College Park, MD, USA
Ak, Ronay
Systems Integration Division, Engineering Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, MD, USA
Lechevalier, David
Le2i, Université de Bourgogne, Dijon, France
Sexton, Thurston
Systems Integration Division, Engineering Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, MD, USA
Mahadevan, Sankaran
Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, TN, USA
Lee, Yung-Tsun Tina
Systems Integration Division, Engineering Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, MD, USA
Pages: 27
Price: Free
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Stock #: SSMS20180018
ISSN: 2520-6478
DOI: 10.1520/SSMS20180018