Statistical aspects in neural network for the purpose of prognostics

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• 作者：Dawn An ; Nam H. Kim ; Joo-Ho Choi
• 关键词：Distribution ; type data ; Johnson distribution ; Neural network ; Prediction uncertainty ; Prognostics
• 刊名：Journal of Mechanical Science and Technology
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：29
• 期：4
• 页码：1369-1375
• 全文大小：585 KB
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• 作者单位：Dawn An (1) (2)
  Nam H. Kim (1)
  Joo-Ho Choi (2)
  
  1. Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, 32611, USA
  2. Department of Aerospace and Mechanical Engineering, Korea Aerospace University, Goyang-si, Gyeonggi-do, 412-791, Korea
  
• 刊物类别：Engineering
• 刊物主题：Mechanical Engineering
  Structural Mechanics
  Control Engineering
  Industrial and Production Engineering
  
• 出版者：The Korean Society of Mechanical Engineers
• ISSN：1976-3824

文摘

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.