A Nonlinear Preventive Maintenance Model with an Environmental Factor Based Weibull Distribution

Gao, Wanli; Ji, Hongbing; Wang, Yu; Yang, Tianshu

doi:10.1088/1757-899x/521/1/012009

Cited by 6 publications

(6 citation statements)

References 19 publications

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“…e importance of the reliability deals with the probability of a particular system performing a particular job for a specific period under the working conditions for which the system is designed. By studying the reliability of the systems, the performance and efficiency of these systems can be evaluated to delineate the type and size of production and improve it through the development of engineering designs for these systems [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Optimal Periods of Conducting Preventive Maintenance to Reduce Expected Downtime and Its Impact on Improving Reliability

Al-Duais

Mohamed

Jawa

et al. 2022

Computational Intelligence and Neuroscience

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The present research mainly aims to use a mathematical formula to determine the optimal intervals for conducting preventive maintenance operations for machines to reduce the expected failure time when the malfunction data follow the Weibull distribution. The reliability function, failure rate, and the average time between machine failures were derived after performing preventive maintenance operations and before conducting preventive maintenance operations to state the amelioration that happens to machines. These rely on real data of performing preventive maintenance operations and the downtime required to repair machine or device faults that occur between preventive maintenance periods and the downtime necessary to perform preventive maintenance operations on the machine or device. Thus, the study concluded that preventive maintenance operations are working to increase the reliability of the machine and improve it, as well as to increase the average period of time for the machine to operate between faults.

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Section: Introductionmentioning

confidence: 99%

Optimal Periods of Conducting Preventive Maintenance to Reduce Expected Downtime and Its Impact on Improving Reliability

Al-Duais

Mohamed

Jawa

et al. 2022

Computational Intelligence and Neuroscience

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“…Recently, Lee et al (2019) used the conditional reliability threshold in place of the system reliability threshold for PM optimization problem. Also, a general maintenance policy with the assumption that PM may be "better-than-new" (BTN), perfect: "as-good-as-new", imperfect: "better-than-old" (BTO) or "as-bad-as-old" (ABAO) were considered by Gao et al (2019). They proposed a nonlinear PM model with scale and shape parameters based on Weibull distribution to describe the effect of PM in the system.…”

Section: Introductionmentioning

confidence: 99%

“…(2019) used the conditional reliability threshold in place of the system reliability threshold for PM optimization problem. Also, a general maintenance policy with the assumption that PM may be “better-than-new” (BTN), perfect: “as-good-as-new”, imperfect: “better-than-old” (BTO) or “as-bad-as-old” (ABAO) were considered by Gao et al . (2019).…”

Section: Introductionmentioning

confidence: 99%

Geometric imperfect preventive maintenance and replacement (GIPMAR) model for aging repairable systems

Udoh

Effanga

2021

IJQRM

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PurposeThis work seeks to develop a geometric imperfect preventive maintenance (PM) and replacement model (GIPMAR) for aging repairable systems due to age and prolong usage that would meet users need in three phases: within average life span, beyond average life span and beyond initial replacement age of system.Design/methodology/approachThe authors utilized the geometric process (GP) as the hazard function to characterize the increasing failure rate (IFR) of the system. The GP hazard function was incorporated into the hybridized preventive and replacement model of Lin et al. (2000). The resultant expected cost rate function was optimized to obtain optimum intervals for PM/replacement and required numbers of PM per cycle. The proposed GIPMAR model was applied to repairable systems characterized by Weibull life function and the results yielded PM/replacement schedules for three different phases of system operation.FindingsThe proposed GIPMAR model is a generalization of Lin et al. (2000) PM model that were comparable with results of earlier models and is adaptive to situations in developing countries where systems are used across the three phases of operation depicted in this work. This may be due to economic hardship and operating environment.Practical implicationsThe proposed model has provided PM/Replacement schedules for different phases of operation which was never considered. This would provide a useful guide to maintenance engineers and end-users in developing countries with a view to minimizing the average cost of maintenance as well as reducing the number of down times of systems.Social implicationsA duly implemented GIPMAR model would ensure efficient operation of systems, optimum man-hour need in the organization and guarantee customer's goodwill in a competitive environment.Originality/valueIn this work, the authors have extended Lin et al. (2000) PM model to provide PM/replacement schedules for aging repairable systems which was not provided for in earlier existing models and literature.

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“…Wu 35 summarized some existing PM models mainly from the point of the failure intensity function and analysed their limitations on application. Gao et al 36 proposed a failure intensity function with shape and scale adjustable parameters based on Weibull distribution to model the PM effect. Wu 17 used a doubly GP to overcome the limitations of the current research, and the proposed model can modify the change of the shape parameter.…”

Section: Related Workmentioning

confidence: 99%

An extended geometric process and its application in replacement policy

Gao

2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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In this article, we develop an extended geometric process model with a recovery factor and propose a two-dimensional evaluation function for each maintenance effect. The developed model can describe the mean inter-failure time which can be improved by the first or several former repairs in some repairable systems. We also propose parameter estimation methods for the developed geometric process model and use some real failure datum to prove the model’s application. Focused on the developed geometric process model, two novel replacement policies and their optimizations are also presented in detail. Then, two real case studies are discussed to illustrate the modelling and optimizing process. Modelling results show that each recovery factor is related to the effect of each repair, and the developed geometric process model has a good-fitting property for some real cases. Optimization results display that the optimal ( N, m) replacement policy is applicable for some small-sized manufacturing systems, whereas the proposed ( N) replacement policy is suitable for some expensive systems.

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A Nonlinear Preventive Maintenance Model with an Environmental Factor Based Weibull Distribution

Cited by 6 publications

References 19 publications

Optimal Periods of Conducting Preventive Maintenance to Reduce Expected Downtime and Its Impact on Improving Reliability

Optimal Periods of Conducting Preventive Maintenance to Reduce Expected Downtime and Its Impact on Improving Reliability

Geometric imperfect preventive maintenance and replacement (GIPMAR) model for aging repairable systems

An extended geometric process and its application in replacement policy

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