Condition-based maintenance decision based on inverse gaussian deterioration process under the condition of regular detection and maintenance

Abstract

As the existing research of condition-based Maintenance (CBM) decision-making neglects the influence of regular detection and maintenance (RDM) on the recovery of equipment performance, it is impossible to accurately describe the state degradation characteristics and life distribution law in this case, which is not helpful to formulate reasonable and effective maintenance strategies. Aimed at this problem, a maintenance strategy combining RDM and CBM is proposed in this paper, and the performance degradation modeling and maintenance optimization model under this strategy are studied deeply. Considering the discontinuous and catastrophic performance degradation characteristics of equipment under this condition, a performance degradation model is established by using the Inverse Gaussian process from the failure mechanism. On this basis, a combined maintenance decision model constrained by risk function is constructed. The optimal maintenance cycle and preventive maintenance threshold are obtained by optimizing the equipment maintenance cost under long-term operation conditions. The relationship between the cost rate and the maintenance strategy value is obtained through the example analysis of the equipment components, and it is proved that the joint maintenance strategy can not only prolong the service life and maintenance interval of equipment, but also reduce the maintenance risk and cost.

Keywords

Regular detection and maintenance condition-based maintenance inverse gaussian process cost rate maintenance strategy

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References

Garg

, Bi-criteria optimization for finding the optimal replacement interval for maintaining the performance of the process industries, Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics, IGI Global, 2016, pp. 643–675.

Alaswad

and Xiang

, A review on condition-based maintenance optimization models for stochastically deteriorating system, Reliability Engineering & System Safety 157 (2017), 54–63.

Garg

, Rani

and Sharma

S.P.

, Preventive maintenance scheduling of the pulping unit in a paper plant, Japan Journal of Industrial and Applied Mathematics 30(2) (2013), 397–414.

Garg

, Reliability, availability and maintainability analysis of industrial systems using PSO and fuzzy methodology, Mapan 29(2) (2014), 115–129.

Garg

, A novel approach for analyzing the reliability of series-parallel system using credibility theory and different types of intuitionistic fuzzy numbers, (3), Journal of the Brazilian Society of Mechanical Sciences and Engineering 38 (2016), 1021–1035.

Garg

, Performance analysis of an industrial system using soft computing based hybridized technique, Journal of the Brazilian Society of Mechanical Sciences and Engineering 39(4) (2017), 1441–1451.

Liao

, Chen

and Yang

, Joint optimization of preventive maintenance and production scheduling for parallel machines system, Journal of Intelligent & Fuzzy Systems 32(1) (2017), 913–923.

, Voisin

, Levrat

, et al., A proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions, Reliability Engineering & System Safety 133(11) (2015), 22–32.

, Guoqing

and Bingxiang

, Accelerated degradation system model based on Gamma process and its optimal condition-maintenance strategy, Computer Integrated Manufacturing Systems 19(11) (2013), 2922–2927.

10.

Pei

, Si

, Hu

, et al., A multi-stage Wiener process-based prognostic model for equipment considering the influence of imperfect maintenance activities, Journal of Intelligent & Fuzzy Systems 34(6) (2018), 3695–3705.

11.

, Hu

, Zhou

, et al., Optimal detection strategy for storage equipment based on degenerate data, Systems Engineering and Electronics 37(5) (2015), 1219–1223.

12.

Ram

and Garg

, An approach for analyzing the reliability and profit of an industrial system based on the cost free warranty policy, Journal of the Brazilian Society of Mechanical Sciences and Engineering 40(5) (2018), 1–9.

13.

Farnoosh Naderkhani

Z.G.

and Makis

, Optimal condition-based maintenance policy for a partially observable system with two sampling intervals, International Journal of Advanced Manufacturing Technology 78(5-8) (2015), 795–805.

14.

Chen

, Ye

Z.S.

, Xiang

, et al., Condition-based maintenance using the inverse Gaussian degradation model, European Journal of Operational Research 243(1) (2015), 190–199.

15.

Z.S.

and Chen

, The inverse gaussian process as a degradation model, Technometrics 56(3) (2014), 302–311.

16.

Wang

and Xu

, An inverse gaussian process model for degradation data, Technometrics 52(2) (2010), 188–197.

17.

Peng

, Li

Y.F.

, Yang

Y.J.

, et al., Inverse Gaussian process models for degradation analysis: A Bayesian perspective, Reliability Engineering & System Safety 130(1) (2014), 175–189.

18.

, Zhang

, Zhong

, et al., Performance deterioration modeling and optimal preventive maintenance strategy under scheduled servicing subject to mission time, Chinese Journal of Aeronautics 27(4) (2014), 821–828.

19.

Doostparast

, Kolahan

and Doostparast

, A reliability-based approach to optimize preventive maintenance scheduling for coherent systems, Reliability Engineering & System Safety 126 (2014), 98–106.

20.

Wang

, Deng

, Hu

, et al., Preventive maintenance decision for complex mechanical equipment based on multiple failure modes, Computer integrated manufacturing system 21(9) (2015), 2504–2514.

21.

Marlow

D.R.

, Beale

D.J.

and Mashford

J.S.

, Risk-based prioritization and its application to inspection of valves in the water sector, Reliability Engineering & System Safety 100 (2012), 67–74.

22.

Haowei

, Kenan

and Junliang

, Life prediction of missile components based on multi parameter correlation degradation under random environmental stress impact, Acta Aeronautical Acta 37(11) (2016), 3404–3412.