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Estimating Remaining Useful Life of Turbofan Engine Using Data-Level Fusion and Feature-Level Fusion

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Abstract

Degradation of systems is a natural and inevitable process which widely happens in industries. Therefore, prognostics is used to prevent unexpected failures of complex engineering systems by evaluating the health status of the system and estimating the remaining useful life, using multiple sensors that simultaneously monitor the degradation process of the system. Since these sensor signals often have usable and effective information about a degradation process, data fusion methods are used to obtain more precise and reliable prognostic results. In this paper, the problem of prognostic modeling and remaining useful life estimation of the turbofan engine is considered and a methodology is proposed using data-level and feature-level fusion approaches to better characterize the degradation process of the system. A degradation model for the engine fault is developed by combining a physics-based model and a Wiener process with positive drift. The maximum likelihood estimation method is used to estimate the unknown parameters of the model. Eventually, the remaining useful life is estimated for testing data of a case study involving the degradation of a turbofan engine, and the results are evaluated.

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Authors and Affiliations

  1. Control Engineering Department, Islamic Azad University Science and Research Branch, Tehran, Iran

    Sheida Ghorbani

  2. Control and Instrumentation Department, Petroleum University of Technology, Tehran, Iran

    Karim Salahshoor

Authors
  1. Sheida Ghorbani
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  2. Karim Salahshoor
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Correspondence to Sheida Ghorbani.

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Ghorbani, S., Salahshoor, K. Estimating Remaining Useful Life of Turbofan Engine Using Data-Level Fusion and Feature-Level Fusion. J Fail. Anal. and Preven. 20, 323–332 (2020). https://doi.org/10.1007/s11668-020-00832-x

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  • DOI: https://doi.org/10.1007/s11668-020-00832-x

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