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Statistical distributions comparison for remaining useful life prediction of components via ANN


Remaining useful life prediction of a system is one of the most important and critical items to achieve the optimal condition-based maintenance for availability and reliability increase, and maintenance reduction. We develop an artificial neural network (ANN) based approach to increase accuracy for the prediction of a system/component remaining useful life. The ANN model takes the working time/age and some parameters produced by a condition monitoring process at the present and previous inspection points as the inputs, and the percentage of the life is produced as the output. Also, different distribution functions, such as Weibull, Birnbaum–Saunders, Gamma, Wakeby, Logistic and Log–normal function are utilized to adjust each condition monitoring data for a failure history, and the adjusted values are applied to determine the training set so as to decrease the noise factors influences unrelated to the degradation of the equipment. The ANN method is validated using the collected vibration monitoring data from a particular machine (pump bearings in the field). Finally, the performance of the distribution functions on the results of the ANN output is compared and the more effective functions are defined.

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(adapted from Tian 2012)

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The authors would like to thank the associate editor and anonymous reviewers for their constructive comments which have led to an improvement to an earlier version of the paper.

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Correspondence to Mohammad Ali Farsi.

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Farsi, M.A., Masood Hosseini, S. Statistical distributions comparison for remaining useful life prediction of components via ANN. Int J Syst Assur Eng Manag 10, 429–436 (2019).

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  • Remaining useful life
  • Distribution functions
  • Prediction
  • Bearing
  • Artificial neural network