Leila Jafari

Optimal maintenance policy and residual life estimation for a slowly degrading system subject to condition monitoring

In this paper, we present an optimal preventive maintenance policy and develop a procedure for residual life estimation for a slowly degrading system subject to soft failure and condition monitoring at equidistant, discrete time epochs. An autoregressive model with time effect is considered to describe the system degradation, which utilizes both the system current age and the previous state observations. The class of control-limit maintenance policies with two different inspection strategies is considered, and the optimization problem is formulated and solved in a semi-Markov decision process framework. The objective is to minimize the long-run expected average cost. A formula for the mean residual life is derived for the proposed degradation model and a control limit policy, which enables the estimation of the remaining useful life and early maintenance planning based on the observed degradation process. An example is presented to demonstrate the effectiveness of the proposed method.

Joint optimization of maintenance policy and inspection interval for a multi-unit series system using proportional hazards model

Unlike the previous maintenance models of multi-unit systems which considered condition-based maintenance (CBM) or age information separately, we propose a novel optimization model which is characterized by a combination of CBM and age information using proportional hazards model. The preventive maintenance is applied for the main two units, where one unit is the core part of the system and subject to CM, and only the age information for the second main unit is available. Also, the other units are adjusted or replaced each time when the system is maintained. The objective is to find an optimal opportunistic maintenance policy minimizing the long-run expected average cost per unit time. The problem is formulated and solved in the semi-Markov decision process framework. The formula for the mean residual life of the system is derived, which is an important statistic in practical applications. A practical example of a multi-unit system from a mining company is provided, and a comparison with other policies shows an outstanding performance of the new model and the control policy proposed in this paper.

Optimal CBM policy with two sampling intervals

Purpose The purpose of this paper is to propose a novel condition-based maintenance (CBM) policy with two sampling intervals for a system subject to stochastic deterioration described by the Cox’s proportional hazards model (PHM). Design/methodology/approach In this paper, the new or renewed system is monitored using a longer sampling interval. When the estimated hazard function of the system exceeds a warning limit, the observations are taken more frequently, i.e., the sampling interval changes to a shorter one. Preventive maintenance is performed when either the hazard function exceeds a maintenance threshold or the system age exceeds a pre-determined age. A more expensive corrective maintenance is performed upon system failure. The proposed model is formulated in the semi-Markov decision process (SMDP) framework. Findings The optimal maintenance policy is found and a computational algorithm based on policy iteration for SMDP is developed to obtain the control thresholds as well as the sampling intervals minimizing the long-run expected average cost per unit time. Research limitations/implications A numerical example is presented to illustrate the whole procedure. The newly proposed maintenance policy with two sampling intervals outperforms previously developed maintenance policies using PHM. The paper compares the proposed model with a single sampling interval CBM model and well-known age-based model. Formulas for the conditional reliability function and the mean residual life are also derived for the proposed model. Sensitivity analysis has been performed to study the effect of the changes in the Weibull parameters on the average cost. Practical implications The results show that considerable cost savings can be obtained by implementing the maintenance policy developed in this paper. Originality/value Unlike the previous CBM policies widely discussed in the literature which use sequential or periodic monitoring, the authors propose a new sampling strategy based on two sampling intervals. From the economic point of view, when the sampling is costly, it is advantageous to monitor the system less frequently when it is in a healthy state and more frequently when it deteriorates and enters the unhealthy state.