TITLE:
A Valorized Scheme for Failure Prediction Using ANFIS: Application to Train Track Breaking System
AUTHORS:
Tse Sparthan, Wolfgang Nzie, Bertin Sohfotsing, Tibi Beda, Olivier Garro
KEYWORDS:
Failure Prediction (FP), Remaining Useful Life (RUL), Artificial Intelligence (AI), Traintrack System, ANFIS, Modeling
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.10 No.11,
November
27,
2020
ABSTRACT: In the rolling stock sector, the ability to protect
passengers, freight and services relies on heavy inborn maintenance. Initiating
an accurate model suitable to foresee the change of attitude on components when
operating rolling stock systems will assist in reducing lock down and favors
heavy productivity. In that light, this paper showcases a suitable methodology
to track degradation of components through the blinding of physic laws and
artificial intelligent techniques. This model used to foresee failure
deterioration rate and remaining useful life (RUL) speculation is case study to
showcase its quality and perfection, within which behavioral data are obtained
through simulated models initiated in Mathlab. For feature extraction and
forecasting issues, different neuro-fuzzy inference systems are designed,
learnt and authenticated with powerful outputs gained during this process.