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Statistical aspects in neural network for the purpose of prognostics

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Abstract

Neural network (NN) is a representative data-driven method, which is one of prognostics approaches that is to predict future damage/degradation and the remaining useful life of in-service systems based on the damage data measured at previous usage conditions. Even though NN has a wide range of applications, there are a relatively small number of literature on prognostics compared to the usage in other fields such as diagnostics and pattern recognition. Especially, it is difficult to find studies on statistical aspects of NN for the purpose of prognostics. Therefore, this paper presents the aspects of statistical characteristics of NN that are presumable in practical usages, which arise from measurement data, weight parameters related to the neural network model, and loading conditions. The Bayesian framework and Johnson distribution are employed to handle uncertainties, and crack growth problem is addressed as an example.

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, 32611, USA

    Dawn An & Nam H. Kim

  2. Department of Aerospace and Mechanical Engineering, Korea Aerospace University, Goyang-si, Gyeonggi-do, 412-791, Korea

    Dawn An & Joo-Ho Choi

Authors
  1. Dawn An
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  2. Nam H. Kim
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  3. Joo-Ho Choi
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Correspondence to Joo-Ho Choi.

Additional information

Recommended by Guest Editor Joo-Ho Choi

Dawn An received the B.S. degree and M.S. degree of mechanical engineering from Korea Aerospace University in 2008 and 2010, respectively. She is now a joint Ph.D. student at Korea Aerospace University and the University of Florida. Her current research is focused on prognostics methods for practical considerations, such as a large noise in data, insufficient number of data, and indirectly measured data.

Nam-Ho Kim received the B.S. degree of mechanical engineering from Seoul National University in 1989, the M.S. and Ph.D. degrees of mechanical engineering from Korea Advanced Institute of Science and Technology (KAIST) in 1991 and the University of Iowa in 1999, respectively. He worked as a Postdoctoral Associate at the University of Iowa from 1999 to 2001. He joined the Dept. of Mechanical & Aerospace Engineering at the University of Florida, in 2002 and is now Professor. His current research is focused on design under uncertainty, design optimization of automotive NVH problem, shape DSA of transient dynamics (Implicit/explicit) and structural health monitoring.

Joo-Ho Choi received the B.S. degree of mechanical engineering from Hanyang University in 1981, the M.S. and Ph.D. degrees of mechanical engineering from Korea Advanced Institute of Science and Technology (KAIST) in 1983 and 1987, respectively. During the year 1988, he worked as a Postdoctoral Fellow at the University of Iowa. He joined the School of Aerospace and Mechanical Engineering at Korea Aerospace University, Korea, in 1997 and is now Professor. His current research is focused on the reliability analysis, design for lifetime reliability, and prognostics and health management.

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An, D., Kim, N.H. & Choi, JH. Statistical aspects in neural network for the purpose of prognostics. J Mech Sci Technol 29, 1369–1375 (2015). https://doi.org/10.1007/s12206-015-0306-8

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  • DOI: https://doi.org/10.1007/s12206-015-0306-8

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