Reliability evaluation for colliery machine based on fuzzy interval model

Jump To References Section

Authors

  • ,CN
  • ,CN

DOI:

https://doi.org/10.18311/jmmf/2021/28075

Keywords:

Reliability evaluation, colliery machine, antifriction bearing, Weibull distribution model, fuzzy interval

Abstract

The mechanical reliability design method is a common method, and it is the most direct and effective method to carry out the mechanical reliability design at present. The reliability optimization design is an effective optimization design method which is developed in combination with the reliability design theory on the basis of the conventional optimization design. Taking the antifriction bearing as an example, this paper systematically expounds all kinds of mechanical reliability design methods. Through comprehensive analysis of various factors that affect the reliability of colliery machine, the index system of colliery machine reliability evaluation is established. Because of the complexity and diversity of the use of colliery machine in the evaluation system, and the fuzziness of human thinking, it is difficult to give the deterministic evaluation information in numerical form, so this paper also analyzes the design method of colliery mechanical reliability, and puts forward an interval fuzzy evaluation method for colliery mechanical reliability evaluation to avoid overload operation of colliery machine and ensure safety production and safety of workers. The simulation results show that the inherent law of reliability is effectively characterized by this method, and it provides a evidence for security produce and scientific decision-making of colliery mechine.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Downloads

Published

2021-07-02

How to Cite

Yang, L., & Chen, C. (2021). Reliability evaluation for colliery machine based on fuzzy interval model. Journal of Mines, Metals and Fuels, 69(5), 149–154. https://doi.org/10.18311/jmmf/2021/28075

Issue

Section

Articles
Received 2021-07-02
Accepted 2021-07-02
Published 2021-07-02

 

References

P Karthikeyan, S Vasuki. (2016): Hybrid Approach of Efficient Decision-Based Algorithm and Fuzzy Logic for the Removal of High Density Salt and Pepper Noise in Images. Journal of Circuits Systems & Computers, 25 (10), 130-145.

MA Butt, M Akram. (2016): A Novel Fuzzy Decision- Making System for CPU Scheduling Algorithm. Neural Computing & Applications, 27 (7), 1-13.

A Pahlavani. (2017): A Hybrid Algorithm Of Improved Case-Based Reasoning And Multi-Attribute Decision Making In Fuzzy Environment For Investment Loan Evaluation. International Journal of Information & Decision Sciences, 2 (1), 17-49.

Y Zhao, Y Cai, Q Song (2018): Energy control of plugin hybrid electric vehicles using model predictive control with route preview. IEEE/CAA Journal of Automatica Sinica, DOI: 10.1109/JAS.2017.7510889, 1-8.

Yang Zhao, Zhenghong Guo, Jianming Yan. (2017): Vibration Signal Analysis and Fault Diagnosis of Bogies of the High-speed Train based on Deep Neural Networks. Journal of Vibroengineering, 19(4), 2456-2474.

MAB Messaoud, A Bouzid, N Ellouze. (2016): A New Biologically Inspired Fuzzy Expert System-Based Voiced/Unvoiced Decision Algorithm for Speech Enhancement. Cognitive Computation, 8 (3), 478-493.

Chen Shyi-Ming, Lee Chia-Hoang. (2014): New Methods for Students' Evaluation using Fuzzy Sets. Fuzzy Sets and Systems, 10(2), 200-218.

Ghondaghsaz, Fatemeh, Rasekh, Abdolrahman. (2012): Case-Deletion Diagnostics for Testing a Linear Hypothesis about Weighted Regression Coefficients. International Journal of Intelligent Technologies & Applied Statistics, 5(3), 237-248.

F Tiryaki, B Ahlatcioglu. (2015): Fuzzy Portfolio Selection using Fuzzy Analytic Hierarchy Process. Information Sciences, 179 (2), 53-69.

Yang Zhao, Yanguang Cai, Guobing Fan. (2016): Dynamical Behaviour for Fractional-order Shunting Inhibitory Cellular Neural Networks. Journal of Nonlinear Science and Applications, 9(6), 4589-4599.