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Remaining useful life estimation by stochastic Markov model and Monte-Carlo simulation

Author

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  • Smriti Mishra
  • Prashant Bhardwaj

Abstract

Estimation of remaining tool life is important in the planning of condition based maintenance program and helped in preventing any production loss. In this paper, a method has been proposed to estimate the remaining useful life (RUL) of a single point turning tool using stochastic Markov method. For this purpose, mild steel workpiece was machined for a constant length on a lathe machine using a high-speed steel (HSS) tool. The flank wear width of tool for multiple passes over the workpiece was recorded for constant feed, speed, and depth of cut, up to the failure of the tool. A state-based model is developed considering four gradually degraded stages of the tool. The rate equations are derived for four state Markov model representing the probabilities of the state change with respect to time. The Runge-Kutta method is used to solve the state change equations using MATLAB. The verification of analytical results was carried out by Monte Carlo simulation. The results obtained from the simulations are accurately matching with experimental results. Therefore, the RUL of a turning tool can be predicted accurately using this proposed model.

Suggested Citation

  • Smriti Mishra & Prashant Bhardwaj, 2021. "Remaining useful life estimation by stochastic Markov model and Monte-Carlo simulation," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 39(1), pages 59-70.
    • Handle: RePEc:ids:ijisen:v:39:y:2021:i:1:p:59-70
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