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

Mohammad Ali Farsi () and S. Masood Hosseini
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Mohammad Ali Farsi: Ministry of Science, Research and Technology
S. Masood Hosseini: Ministry of Science, Research and Technology

International Journal of System Assurance Engineering and Management, 2019, vol. 10, issue 3, No 10, 429-436

Abstract: Abstract 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.

Keywords: Remaining useful life; Distribution functions; Prediction; Bearing; Artificial neural network (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s13198-019-00813-w

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