System Maintenance Method Based on the Mean Residual Life-Importance Measure

2021 ◽  
pp. 137-148
Author(s):  
Zhang Zhengxin ◽  
Gao Hengyi ◽  
Cheng Luming ◽  
Li Xiaohua ◽  
Deng Qianbao
Keyword(s):  
Residual Life ◽  
Importance Measure ◽  
Mean Residual Life ◽  
System Maintenance ◽  
The Mean

scholarly journals Limiting behaviour of the mean residual life

2003 ◽  
Vol 55 (1) ◽  
pp. 217-226 ◽  
Author(s):  
David M. Bradley ◽  
Ramesh C. Gupta
Keyword(s):  
Residual Life ◽  
Mean Residual Life ◽  
Limiting Behaviour ◽  
The Mean

A degradation model for maintenance improvement in respect of cost and availability

2015 ◽  
Vol 21 (1) ◽  
pp. 55-69 ◽  
Author(s):  
Christophe Letot ◽  
Pierre Dehombreux ◽  
Edouard Rivière-Lorphèvre ◽  
Guillaume Fleurquin ◽  
Arnaud Lesage
Keyword(s):  
Preventive Maintenance ◽  
Residual Life ◽  
Operating Time ◽  
Hitting Times ◽  
Mean Residual Life ◽  
Reliability Model ◽  
Content Type ◽  
Degradation Data ◽  
Specific Item ◽  
The Mean

Purpose – The purpose of this paper is to highlight the need for degradation data in order to improve the reliability and the mean residual life estimation of a specific item of equipment and to adapt the preventive maintenance tasks accordingly. Design/methodology/approach – An initial reliability model which uses a degradation-based reliability model that is built from the collection of hitting times of a failure threshold. The proposed maintenance model is based on the cost/availability criterion. The estimation of both reliability and optimum time for preventive maintenance are updated with all new degradation data that are collected during operating time. Findings – An improvement for the occurrences of maintenance tasks which minimizes the mean cost per unit of time and increases the availability. Practical implications – Inspection tasks to measure the degradation level should be realized at least one time for each item of equipment at a specific time determined by the proposed methodology. Originality/value – The introduction of a criterion which helps the maintainer to decide to postpone or not the preventive replacement time depending on the measured degradation level of a specific item of equipment.

MEAN RESIDUAL LIFE FUNCTION FOR ADDITIVE AND MULTIPLICATIVE HAZARD RATE MODELS

2015 ◽  
Vol 30 (2) ◽  
pp. 281-297 ◽  
Author(s):  
Ramesh C. Gupta
Keyword(s):  
Hazard Rate ◽  
Residual Life ◽  
Turning Points ◽  
Sufficient Conditions ◽  
Mean Residual Life ◽  
Residual Life Function ◽  
Mean Residual Life Function ◽  
Hazard Rate Models ◽  
The Mean ◽  
Life Function

This paper deals with the mean residual life function (MRLF) and its monotonicity in the case of additive and multiplicative hazard rate models. It is shown that additive (multiplicative) hazard rate does not imply reduced (proportional) MRLF and vice versa. Necessary and sufficient conditions are obtained for the two models to hold simultaneously. In the case of non-monotonic failure rates, the location of the turning points of the MRLF is investigated in both the cases. The case of random additive and multiplicative hazard rate is also studied. The monotonicity of the mean residual life is studied along with the location of the turning points. Examples are provided to illustrate the results.

scholarly journals An Efficient Estimation of the Mean Residual Life Function with Length-Biased Right-Censored Data

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Hongping Wu ◽  
Yihui Luan
Keyword(s):  
Monte Carlo ◽  
Censored Data ◽  
Residual Life ◽  
Random Variable ◽  
Efficient Estimation ◽  
Mean Residual Life ◽  
Right Censored Data ◽  
Monte Carlo Simulation Study ◽  
The Mean ◽  
Model Setup

The mean residual life (MRL) function for a lifetime random variableT0is one of the basic parameters of interest in survival analysis. In this paper, we propose a new estimator of the MRL function with length-biased right-censored data and evaluate its performance through a small Monte Carlo simulation study. The results of the simulations show that the proposed estimator outperforms the existing one referred to in Data and Model Setup Section in terms of Monte Carlo bias and mean square error, especially when the censoring rate is heavy. We also show that the proposed estimator converges in distribution under some conditions.

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