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Simulation Based Machine Learning for Fault Detection in Complex Systems Using the Functional Failure Identification and Propagation Framework

[+] Author Affiliations
Nikolaos Papakonstantinou

Aalto University, Espoo, Finland

Scott Proper, Irem Y. Tumer

Oregon State University, Corvallis, OR

Bryan O’Halloran

Raytheon Missile Systems, Tucson, AZ

Paper No. DETC2014-34628, pp. V01BT02A022; 10 pages
doi:10.1115/DETC2014-34628
From:
  • ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1B: 34th Computers and Information in Engineering Conference
  • Buffalo, New York, USA, August 17–20, 2014
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-4629-2
  • Copyright © 2014 by ASME

abstract

Fault detection and identification in mechatronic systems with complex interdependencies between subsystems is a very active research area. Various alternative quantitative and qualitative methods have been proposed in the literature for fault identification on industrial processes, making it difficult for researchers and industrial practitioners to choose a method for their application. The Functional Failure Identification and Propagation (FFIP) framework has been proposed in past research for risk assessment of early complex system designs. FFIP is a versatile framework which has been extended in prior work to automatically evaluate sets of alternative system designs, perform sensitivity analysis, and event trees generation from critical event scenario simulation results. This paper’s contribution is an FFIP extension, used to generate the training and testing data sets needed to develop fault detection systems based on data driven machine learning methods. The methodology is illustrated with a case study of a generic nuclear power plant where a fault or the location of a fault within the system is identified. Two fault detection methods are compared, based on an artificial neural network and a decision tree. The case study results show that the decision tree was more meaningful as a model and had better detection accuracy (97% success in identification of fault location).

Copyright © 2014 by ASME

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