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Estimating Probability Distribution of Remaining Useful Life of Rolling Element Bearing, Using Data-driven Methods

Mollaali, Amirhossein | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 51805 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Behzad, Mehdi
  7. Abstract:
  8. Predicting the probability distribution of asset remaining useful life is an essential procedure in the intelligent maintenance. It also plays an important role in improving system reliability and optimizing further decisions. The main concern of this project is to estimate the probability distribution of rolling element bearing remaining useful life. For this purpose, the bearing degradation process is modeled through the statistical models, considering the major variabilities in the degradation process. The models parameters are updated, once a new measurement of the equipment is available. Then, the constructed model is utilized in order to predict the probabitity distribution of remaining useful life, through the probabilistic inference. In the first section of project, a new method is proposed in order to improve the parameter estimation in the adaptive nonlinear model. The performed predictions based on the proposed method, have more or at least similar accuracy in comparision to the previous models, generally. In another section of the project, the feature uncertainty is introduced and a new methodology is presented in order to consider it (in presence of simultaneous multi features). Utilizing the proposed method on the experimental data exhibits a more practical vision about the asset future
  9. Keywords:
  10. Probability Distribution Function ; Statistical Model ; Rolling Bearing ; Bayesian Framework ; Kalman Filters ; Remaining Useful Life ; Maximum Likelihood Estimation

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