Abstract

Condition-based maintenance has been widely used in the maintenance strategy of equipment or systems. Aiming at the maintenance decision-making of the equipment with dynamic degradation characteristics, a dynamic condition-based maintenance model is proposed based on Inverse Gaussian process in this paper. Firstly, an Inverse Gaussian process with stochastic parameter is proposed to describe the change of the equipment degradation characteristics during operation, and the important stochastic properties related to condition-based maintenance is deduced. Secondly, the dynamic maintenance threshold function is proposed, and its different values at different degradation stages can reduce the early failure risk of the equipment while ensuring a lower expected cost ratio. On this basis, a dynamic maintenance decision-making model with multi-objectives is established. A numerical example is illustrated to verify the correctness and practicability of the proposed method, and the sensitivity analysis results of the related parameters prove the necessity of considering the dynamic degradation characteristics of equipment. The comparison result proves that the method proposed in this paper can obtain better safety and economy of maintenance than the method with fixed thresholds.

Highlights

  • With the intensification of market competition in the context of economic globalization, enterprises are increasingly demanding the precision and reliability of production equipment

  • The variance of drift parameters of Inverse Gaussian (IG) process decreases with time, which reflects that the degradation rate of equipment will change during operation, and the uncertainty of our understanding to this change will decrease with the enrichment of the acquired condition information

  • In the first PM cycle after the equipment is put into production, we find that the equipment is with higher degradation rate at the initial stage of operation through dynamic maintenance threshold function (DMTF)

Read more

Summary

A Dynamic Condition-Based Maintenance Model Using Inverse Gaussian Process

Key Laboratory of Advanced Forging & Stamping Technology and Science of Ministry of Education of China, Yanshan University, Qinhuangdao 066004, China Hebei Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China

INTRODUCTION
PROBLEM FORMULATION
THE IMPERFECT PM MODEL AND ITS INFLUENCES
MAINTENANCE DECISION-MAKING MODEL
MAINTAINED SYSTEM EVOLUTION
A NUMERICAL EXAMPLE
CONCLUSION
PROOF OF THEOREM 1
PROOF OF THEOREM 2
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call