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A Valorized Scheme for Failure Prediction Using ANFIS: Application to Train Track Breaking System

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Document pages: 26 pages

Abstract: In the rolling stock sector, the ability to protectpassengers, freight and services relies on heavy inborn maintenance. Initiatingan accurate model suitable to foresee the change of attitude on components whenoperating rolling stock systems will assist in reducing lock down and favorsheavy productivity. In that light, this paper showcases a suitable methodologyto track degradation of components through the blinding of physic laws andartificial intelligent techniques. This model used to foresee failuredeterioration rate and remaining useful life (RUL) speculation is case study toshowcase its quality and perfection, within which behavioral data are obtainedthrough simulated models initiated in Mathlab. For feature extraction andforecasting issues, different neuro-fuzzy inference systems are designed,learnt and authenticated with powerful outputs gained during this process.

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