Sharareh Taghipour

Periodic inspection optimization model for a complex repairable system

This paper proposes a model to find the optimal periodic inspection interval on a finite time horizon for a complex repairable system. In general, it may be assumed that components of the system are subject to soft or hard failures, with minimal repairs. Hard failures are either self-announcing or the system stops when they take place and they are fixed instantaneously. Soft failures are unrevealed and can be detected only at scheduled inspections but they do not stop the system from functioning. In this paper we consider a simple policy where soft failures are detected and fixed only at planned inspections, but not at moments of hard failures. One version of the model takes into account the elapsed times from soft failures to their detection. The other version of the model considers a threshold for the total number of soft failures. A combined model is also proposed to incorporate both threshold and elapsed times. A recursive procedure is developed to calculate probabilities of failures in every interval, and expected downtimes. Numerical examples of calculation of optimal inspection frequencies are given. The data used in the examples are adapted from a hospitals maintenance data for a general infusion pump.

Prioritization of Medical Equipment for Maintenance Decisions

Clinical engineering departments in hospitals are responsible for establishing and regulating a Medical Equipment Management Program to ensure that medical devices are safe and reliable. In order to mitigate functional failures, significant and critical devices should be identified and prioritized. In this paper, we present a multi-criteria decision-making model to prioritize medical devices according to their criticality. Devices with lower criticality scores can be assigned a lower priority in a maintenance management program. However, those with higher scores should be investigated in detail to find the reasons for their higher criticality, and appropriate actions, such as ‘preventive maintenance’, ‘user training’, ‘redesigning the device’, etc, should be taken. In this paper,we also describe how individual score values obtained for each criterion can be used to establish guidelines for appropriate maintenance strategies for different classes of devices. The information of 26 different medical devices is extracted from a hospitals maintenance management system to illustrate an application of the proposed model.

Reliability analysis of maintenance data for complex medical devices

This paper proposes a method to analyze statistically maintenance data for complex medical devices with censoring and missing information. It presents a classification of the different types of failures and establishes policies for analyzing data at the system and component levels taking into account the failure types. The results of this analysis can be used as basic assumptions in the development of a maintenance/inspection optimization model. As a case study, we present the reliability analysis of a general infusion pump from a hospital. Copyright

Optimal inspection of a complex system subject to periodic and opportunistic inspections and preventive replacements

This paper proposes two optimization models for the periodic inspection of a system with “hard-type” and “soft-type” components. Given that the failures of hard-type components are self-announcing, the component is instantly repaired or replaced, but the failures of soft-type components can only be detected at inspections. A system can operate with a soft failure, but its performance may be reduced. Although a system may be periodically inspected, a hard failure creates an opportunity for additional inspection (opportunistic inspection) of all soft-type components. Two optimization models are discussed in the paper. In the first, soft-type components undergo both periodic and opportunistic inspections to detect possible failures. In the second, hard-type components undergo periodic inspections and are preventively replaced depending on their condition at inspection. Soft-type and hard-type components are either minimally repaired or replaced when they fail. Minimal repair or replacement depends on the state of a component at failure; this, in turn, depends on its age. The paper formulates objective functions for the two models and derives recursive equations for their required expected values. It develops a simulation algorithm to calculate these expected values for a complex model. Several examples are used to illustrate the models and the calculations. The data used in the examples are adapted from a real case study of a hospital’s maintenance data for a general infusion pump.

Optimum inspection interval for a system under periodic and opportunistic inspections

This article proposes a model to find an optimal periodic inspection interval over a finite time horizon for a multi-component system. The system’s components are subject to either hard or soft failures. Hard failures are detected and fixed instantaneously. Soft failures are unrevealed and can only be detected at inspections. Soft failures do not stop the system from operating; however, they may reduce its level of performance from its designed value. The system is inspected periodically to detect soft failures; however, a hard failure instance also provides an opportunity called opportunistic inspection to inspect and fix soft failures. Two models are discussed in this article. The first model assumes that components with soft and hard failures are minimally repaired. The second model assumes the possibility of either minimal repair or replacement of a component with soft failure, with some age-dependent probabilities. Recursive procedures are developed to calculate the expected number of minimal repairs and replacements and expected downtimes of components with soft failure. Examples of the calculation of the optimal inspection intervals are given. The data used in the examples are adapted from a hospital’s maintenance data for general infusion pump.

Joint optimal inspection and inventory for a k-out-of-n system

Abstract Purpose The objective of this paper is to develop a model, which optimizes jointly the inspection frequency and the inventory level for a k-out-of-n system with repairable components whose failures are hidden. Scope The system is periodically inspected to detect failed components, and the components are either minimally repaired or replaced with spares from the inventory. The system fails between periodic inspections if n−k+1 components are down; in that case, all failed components are inspected and rectified if possible. Otherwise, the failed components are rectified at periodic inspections. An emergency spare is ordered at a system failure, if the inventory is empty and all failed components require replacement. Methodology Using analytical approach to find the optimal solution is computationally intensive and not practical; a simulation model is developed to solve the problem. Results The proposed model harmonizes the maintenance and inventory policies and finds the joint optimal solution which results in a minimum total cost. Conclusion The joint optimization model results in a lower cost compared to separate maintenance and inventory optimization models. Novelty Few joint models for k-out-of-n systems exist, and none of them investigate repairable components whose failures are hidden and follow a non-homogeneous Poisson process.

Trend analysis of the power law process using Expectation–Maximization algorithm for data censored by inspection intervals

Abstract Trend analysis is a common statistical method used to investigate the operation and changes of a repairable system over time. This method takes historical failure data of a system or a group of similar systems and determines whether the recurrent failures exhibit an increasing or decreasing trend. Most trend analysis methods proposed in the literature assume that the failure times are known, so the failure data is statistically complete; however, in many situations, such as hidden failures, failure times are subject to censoring. In this paper we assume that the failure process of a group of similar independent repairable units follows a non-homogenous Poisson process with a power law intensity function. Moreover, the failure data are subject to left, interval and right censoring. The paper proposes using the likelihood ratio test to check for trends in the failure data. It uses the Expectation–Maximization (EM) algorithm to find the parameters, which maximize the data likelihood in the case of null and alternative hypotheses. A recursive procedure is used to solve the main technical problem of calculating the expected values in the Expectation step. The proposed method is applied to a hospitals maintenance data for trend analysis of the components of a general infusion pump.

Optimal inspection model for a load-sharing redundant system

In this paper we consider a k-out-of-n load sharing system, in which the failure of a component increases the hazard rates of the surviving components. The components failures follow a power law intensity function. The system is periodically inspected to detect failed components if the number of failures is less than n-k+1. However, the system fails when the number of failures equals to n-k+1, which is when all components are opportunistically inspected and repaired if they are in a failed state. Two models of load-sharing are considered: a tampered failure rate model, in which only the scale parameter of the power law is affected due to a change in load, and the cumulative exposure (CE) model, in which both the scale parameter and the ages of the surviving components are affected. We propose a model to find the optimal inspection interval for such systems, and describe the application of the model in several case studies. The results reveal that a system with the CE model requires to be inspected more frequently to avoid a high penalty incurred due to system failure. Moreover, shorter inspection interval is also required for a system with higher load intensity.

Predictors of competing mortality to invasive breast cancer incidence in the Canadian National Breast Screening study

BackgroundEvaluating the cost-effectiveness of breast cancer screening requires estimates of the absolute risk of breast cancer, which is modified by various risk factors. Breast cancer incidence, and thus mortality, is altered by the occurrence of competing events. More accurate estimates of competing risks should improve the estimation of absolute risk of breast cancer and benefit from breast cancer screening, leading to more effective preventive, diagnostic, and treatment policies. We have previously described the effect of breast cancer risk factors on breast cancer incidence in the presence of competing risks. In this study, we investigate the association of the same risk factors with mortality as a competing event with breast cancer incidence.MethodsWe use data from the Canadian National Breast Screening Study, consisting of two randomized controlled trials, which included data on 39 risk factors for breast cancer. The participants were followed up for the incidence of breast cancer and mortality due to breast cancer and other causes. We stratified all-cause mortality into death from other types of cancer and death from non-cancer causes. We conducted separate analyses for cause-specific mortalities.ResultsWe found that “age at entry” is a significant factor for all-cause mortality, and cancer-specific and non-cancer mortality. “Menstruation length” and “number of live births” are significant factors for all-cause mortality, and cancer-specific mortality. “Ever noted lumps in right/left breasts” is a factor associated with all-cause mortality, and non-cancer mortality.ConclusionsFor proper estimation of absolute risk of the main event of interest common risk factors associated with competing events should be identified and considered.

Parameter estimates for invasive breast cancer progression in the Canadian National Breast Screening Study.

Background:The aim of screening is to detect a cancer in the preclinical state. However, a false-positive or a false-negative test result is a real possibility.Methods:We describe invasive breast cancer progression in the Canadian National Breast Screening Study and construct progression models with and without covariates. The effect of risk factors on transition intensities and false-negative probability is investigated. We estimate the transition rates, the sojourn time and sensitivity of diagnostic tests for women aged 40–49 and 50–59.Results:Although younger women have a slower transition rate from healthy state to preclinical, their screen-detected tumour becomes evident sooner. Women aged 50–59 have a higher mortality rate compared with younger women. The mean sojourn times for women aged 40–49 and 50–59 are 2.5 years (95% CI: 1.7, 3.8) and 3.0 years (95% CI: 2.1, 4.3), respectively. Sensitivity of diagnostic procedures for older women is estimated to be 0.75 (95% CI: 0.55, 0.88), while women aged 40–49 have a lower sensitivity (0.61, 95% CI: 0.42, 0.77). Age is the only factor that affects the false-negative probability. For women aged 40–49, ‘age at entry’, ‘history of breast disease’ and ‘families with breast cancer’ are found to be significant for some of the transition rates. For the age-group 50–59, ‘age at entry’, ‘history of breast disease’, ‘menstruation length’ and ‘number of live births’ are found to affect the transition rates.Conclusion:Modelling and estimating the parameters of cancer progression are essential steps towards evaluating the effectiveness of screening policies. The parameters include the transition rates, the preclinical sojourn time, the sensitivity, and the effect of different risk factors on cancer progression.