Early stage data-based probabilistic wear life prediction and maintenance interval optimization of driving wheels

•Establishing probabilistic wear model and verifying with experiment.•Creating a recursive model to predict wear life using early stage data.•The maintenance ad cost strategies considering the different wheel combinations.•Building multi-objective maintenance interval optimization method under relia...

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Veröffentlicht in:Reliability engineering & system safety 2020-05, Vol.197, p.106791-13, Article 106791
Hauptverfasser: Chang, Mingu, Lee, Jongsoo
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Lee, Jongsoo
description •Establishing probabilistic wear model and verifying with experiment.•Creating a recursive model to predict wear life using early stage data.•The maintenance ad cost strategies considering the different wheel combinations.•Building multi-objective maintenance interval optimization method under reliability. This study presents an early stage data-based maintenance strategy of driving wheels that have different life distributions depending upon their location. Wear was predicted under the condition that the shape of the contact surface changes over time by an original method of back calculating degradation over time through the establishment of a basic wear model and a recursive function for wear progression. An accurate wear model was established and verified by an experiment. The variation in the profile of a wear-induced wheel was applied to the wear model. Furthermore, the model was combined with a recursive function and used to obtain the time-series degradation data. Subsequently, the factors which have a major influence on wheel production were analyzed, and a meta-model was configured using the response surface method. The degradation function and parameter distribution were estimated using uncertainty propagation, and the wear life distribution was derived using Bayesian inference and Markov chain Monte Carlo method. The reliability of driving wheels was obtained, and the maintenance interval was optimized under each maintenance conditions. Based on this novel method, the early stage data-based maintenance strategy was achieved, and the result of the wear life prediction was validated using the probability distribution analysis.
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This study presents an early stage data-based maintenance strategy of driving wheels that have different life distributions depending upon their location. Wear was predicted under the condition that the shape of the contact surface changes over time by an original method of back calculating degradation over time through the establishment of a basic wear model and a recursive function for wear progression. An accurate wear model was established and verified by an experiment. The variation in the profile of a wear-induced wheel was applied to the wear model. Furthermore, the model was combined with a recursive function and used to obtain the time-series degradation data. Subsequently, the factors which have a major influence on wheel production were analyzed, and a meta-model was configured using the response surface method. 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subjects Bayesian analysis
Bayesian inference
Computer simulation
Degradation
Driving wheels
Life prediction
Maintenance
Markov chain Monte Carlo
Markov chains
Monte Carlo simulation
Optimization
Parameter estimation
Parameter uncertainty
Probability distribution
Recursive function
Recursive functions
Reliability engineering
Reliability maintenance interval optimization
Response surface methodology
Statistical analysis
Statistical inference
Wear
Wear life prediction
Wheels
title Early stage data-based probabilistic wear life prediction and maintenance interval optimization of driving wheels
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