Logo

Remaining useful life prediction of drill string using fuzzy systems and cumulative damage theory

Lemma, T.A. and Nanji, P. and Gebremariam, M.A. and Ahsan, S. (2019) Remaining useful life prediction of drill string using fuzzy systems and cumulative damage theory. Key Engineering Materials, 796 . pp. 145-154.

Full text not available from this repository.

Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Drill string failure is a prevalent and costly problem to the oil and gas industry. This paper proposes a method for remaining useful life prediction of drill string components subjected to fatigue under combined loadings, namely axial stress, bending moment, and torsion. To accomplish this, fuzzy systems are used to model the dimensionless stress intensity factor, of different API graded drill pipes. Based on the gathered database of the dimensionless stress intensity factor for various crack types, the parameter is numerically estimated using Adaptive Neuro-Fuzzy Inference System (ANFIS) in MATLAB. The fuzzy model is then incorporated into the available crack growth models (Paris Law & Walker's Law) to quantitatively evaluate the number of cycles as the crack propagates from its initial size to its critical size. The nonlinear crack propagation model is solved by Euler's Method. Finally, a parametric study is performed in order to identify the influence of load magnitudes, the variation of loadings, crack shape, and geometrical parameters on the fatigue life. The ANFIS model developed has a mean square error (MSE) of 8.3e-4, root mean square error (RMSE) of 0.0288 and R-squared error of 0.9807, thus indicating the model is highly reliable. The increase in the magnitude of stress, mean stress ratio (R) and environmental constants reduces the number of cycles to failure, thus indicating shorter RUL. © 2019 Trans Tech Publications, Switzerland

Item Type:Article
Impact Factor:cited By 1
Uncontrolled Keywords:Crack propagation; Cracks; Drill pipe; Drill strings; Drills; Errors; Fatigue of materials; Fuzzy inference; Fuzzy neural networks; Gas industry; Geometry; Infill drilling; Mean square error; Stress intensity factors, Adaptive neuro-fuzzy inference system; ANFIS; Cumulative damage theories; Dimensionless stress intensity factors; Number of cycles to failure; Oil and Gas Industry; Remaining useful life predictions; Root mean square errors, Fuzzy systems
ID Code:23580
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:19 Aug 2021 07:56
Last Modified:19 Aug 2021 07:56

Repository Staff Only: item control page