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A Stochastic Asset Life Prediction Method for Large Fleet Datasets in Big Data Environment

[+] Author Affiliations
Behrad Bagheri, David Siegel, Wenyu Zhao, Jay Lee

University of Cincinnati, Cincinnati, OH

Paper No. IMECE2015-52458, pp. V014T06A010; 9 pages
doi:10.1115/IMECE2015-52458
From:
  • ASME 2015 International Mechanical Engineering Congress and Exposition
  • Volume 14: Emerging Technologies; Safety Engineering and Risk Analysis; Materials: Genetics to Structures
  • Houston, Texas, USA, November 13–19, 2015
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5757-1
  • Copyright © 2015 by ASME

abstract

Preventing catastrophic failures is the most important task of prognostics and health management approaches in industry where Remaining Useful Life (RUL) prediction plays a significant role to schedule required preventive actions. Regarding recent advances and trends in data analysis and in Big Data environment, industries with such foreseeing approach are able to maintain their fleet of assets more efficiently with higher assurance. To address this requirement, several physics-based and data-driven methods have been developed to predict the remaining useful life of various engineering systems. In current paper, we present a simple, yet accurate stochastic method for data-driven RUL prediction of complex engineering system. The approach is constructed based on selecting the most significant parameters from raw data by using the improved distance evaluation method as feature selection algorithms. Subsequently, the health value of units is assessed by logistic regression and the assessment output is used in a Monte Carlo simulation to estimate the remaining useful life of the desired system. During Monte Carlo iterations, several features are extracted to help filtering less accurate estimations and improve the overall prediction accuracy. The proposed algorithm is validated in two ways. First of all, the accuracy of RUL prediction is measured by applying the method to 2008 PHM data challenge gas-turbine dataset. Subsequently, gradual changes in RUL prediction of a particular test unit are measured to verify the behavior of the algorithm upon availability of additional historical data.

Copyright © 2015 by ASME

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