Andrew K. S. Jardine

University of Toronto, Toronto, Ontario, Canada

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Publications (74)45.66 Total impact

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    D. Louit, R. Pascual, D. Banjevic, A. K. S. Jardine
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    Dataset: pascual11g
    R. Pascual, D. Louit, Andrew K. S. Jardine
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    D. Louit, R. Pascual, D. Banjevic, A. K. S. Jardine
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    ABSTRACT: 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.British Journal of Cancer advance online publication, 15 January 2013; doi:10.1038/bjc.2012.596 www.bjcancer.com.
    British Journal of Cancer 01/2013; · 5.08 Impact Factor
  • Tianqiao Tang, Daming Lin, Dragan Banjevic, Andrew K.S. Jardine
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    ABSTRACT: For systems with hidden or unrevealed failures, a common practice is to inspect the system regularly, since failures can only be detected upon inspection. Recent works in the literature have studied the availability of a system under periodic inspection, assuming perfect repair/replacement with non-negligible downtime due to repair/replacement for a detected failure. In some situations, however, not only downtime due to repair/replacement but also downtime due to inspection is non-negligible regardless whether a failure was detected or not. In this paper, we consider the availability of a system subject to hidden failure inspected at constant interval with both non-negligible downtime due to inspection and non-negligible downtime due to repair/replacement.
    Journal of Statistical Planning and Inference 01/2013; 143(1):176–185. · 0.71 Impact Factor
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    ABSTRACT: In this paper we use a five-state model to describe the progression of invasive breast cancer. The states of the model are: 1. Healthy or non-detectable cancer, 2. Preclinical (screening detectable cancer), 3. Clinical (symptoms are evident), 4. Death due to breast cancer, and 5. Death due to causes other than breast cancer. We model the natural progression of breast cancer from healthy state to clinical cancer using a partially observable Markov model. We model the survival time from cancer diagnosis to breast cancer mortality using a Weibull Proportional Hazards Model (PHM). The effect of covariates in both models are also studied. We then combine the two models and develop a simulation model to evaluate the effect of different screening intervals in reducing breast cancer mortality. We use the data from the Canadian National Breast Screening Study (CNBSS), which consists of two randomized screening trials designed to evaluate the effect of mammography on women aged 40-59. The results reveal that screening can be effective in detecting breast cancer at earlier stages, so reducing breast cancer mortality. We estimated a higher reduction for older women.
    Reliability and Maintainability Symposium (RAMS), 2013 Proceedings - Annual; 01/2013
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    ABSTRACT: Mortality due to causes other than breast cancer is a potential competing risk which may alter the incidence probability of breast cancer and as such should be taken into account in predictive modelling. We used data from the Canadian National Breast Screening Study (CNBSS), which consist of two randomized controlled trials designed to evaluate the efficacy of mammography among women aged 40-59. The participants in the CNBSS were followed up for incidence of breast cancer and mortality due to breast cancer and other causes; this allowed us to construct a breast cancer risk prediction model while taking into account mortality for the same study population. In this study, we use 1980-1989 as the study period. We exclude the prevalent cancers from the CNBSS to estimate the probability of developing breast cancer, given the fact that women were cancer-free at the beginning of the follow-up. By the end of 1989, from 89,434 women, 944 (1.1 %) were diagnosed with invasive breast cancer, 922 (1.0 %) died from causes other than breast cancer, and 87,568 (97.9 %) were alive and not diagnosed with invasive breast cancer. We constructed a risk prediction model for invasive breast cancer based on 39 risk factors collected at the time of enrolment or the initial physical examination of the breasts. Age at entry (HR 1.07, 95 % CI 1.05-1.10), lumps ever found in left or right breast (HR 1.92, 95 % CI 1.19-3.10), abnormality in the left breast (HR 1.26, 95 % CI 1.07-1.48), history of other breast disease, family history of breast cancer score (HR 1.01, 95 % CI 1.00-1.01), years menstruating (HR 1.02, 95 % CI 1.01-1.03) and nulliparity (HR 1.70, 95 % CI 1.23-2.36) are the model's predictors. We investigated the effects of time-dependent factors. The model is well calibrated with a moderate discriminatory power (c-index 0.61, 95 % CI 0.59-0.63); we use it to predict the 9-year risk of developing breast cancer for women of different age groups. As an example, we estimated the probability of invasive cancer at 5 years after enrolment to be 0.00448, 0.00556, 0.00691, 0.00863, and 0.01034, respectively, for women aged 40, 45, 50, 55, and 59, all of whom had never noted lumps in their breasts, had 32 years of menstruating, 1-2 live births, no other types of breast disease and no abnormality found in their left breasts. The results of this study can be used by clinicians to identify women at high risk of breast cancer for screening intervention and to recommend a personalized intervention plan. The model can be also utilized by a woman as a breast cancer risk prediction tool.
    Breast Cancer Research and Treatment 06/2012; 134(2):839-51. · 4.47 Impact Factor
  • Nima Safaei, Dragan Banjevic, Andrew K. S. Jardine
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    ABSTRACT: In this paper, an integrated simulation—optimization approach is proposed for annual planning of power restoration workforce related to an electricity distribution company in a province of Canada. Internal and external workforces are employed to perform maintenance actions and restore power after interruptions throughout the province. According to the electricity distribution network, the province is divided into a number of work locations (WL), each having local crews to perform maintenance actions and fix power interruptions. However, determining the size of the crew in each WL over the year is challenging because of high fluctuation in interruption frequency and consequently in projected demand during the year. The frequency of interruptions is affected by various factors such as geographical location, time calendar, and particularly weather conditions. The objective is to determine the optimal combination of internal and external workforce over the year to cover the interruptions across the province with minimum cost and minimum customer interruption duration.
    IEEE Transactions on Power Systems 01/2012; 27(1):442-449. · 2.92 Impact Factor
  • Neil Montgomery, Dragan Banjevic, Andrew K. S. Jardine
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    ABSTRACT: Minor maintenance actions can affect condition-monitoring measurements, which may in turn affect the accuracy of diagnostic and prognostic techniques used in condition-based maintenance (CBM). Outputs of a CBM model include the calculation of optimal maintenance decisions, conditional reliability, and the calculation of remaining useful life, among other measures. It is necessary to have a model for the manner in which the condition monitoring data changes over time to produce these output measures; many models have been developed to do so. It is also common to record minor maintenance actions carried out on critical assets, with lubricant changes being one of the most common, but it is unusual for models to consider the impact of such maintenance actions that affect the condition monitoring data. In this paper we discuss the impact of minor maintenance on CBM models. A dataset on a collection of gearboxes, consisting of reliability and oil analysis information, including data on oil changes and oil additions, is used to illustrate the benefit of modelling minor maintenance actions. KeywordsMaintenance–Decision support systems–Data-driven maintenance model–Condition-based maintenance–Oil analysis–Maintenance models–Remaining useful life–Minor maintenance
    Journal of Intelligent Manufacturing 01/2012; 23(2):303-311. · 1.28 Impact Factor
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    D. Louit, R. Pascual, D. Banjevic, A. K. S. Jardine
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    ABSTRACT: In industries characterized by heavy utilization of equipment and machinery, such as mining, oil & gas, utilities, transportation, adequate stockholding of critical spare parts becomes essential. Insufficient stocks affect overall performance of physical assets, as lack of spares may result in gross penalties, lower availability or increased operational risks. On the other hand, oversized inventories lead to inefficient use of capital and may imply severe expenditures. This paper presents various approaches for the determination of the optimal stock size, when the stock is composed of (i) non-repairable or (ii) repairable parts. The paper is focused on spares for relatively expensive, highly reliable components, rather than on fast-moving spare parts. Optimization criteria considered are minimization of costs, maximization of equipment availability, and the achievement of a desired stock reliability (probability that a spare part request will not be rejected because of the lack of spares in stock). For stock reliability, instantaneous and interval reliability calculations are considered. In addition, models directed to the estimation of the remaining life of a given stock of spare parts (at a certain stock reliability level) are introduced. The paper describes several models subject to practical industrial application, and presents case studies from utilities and mining to illustrate their use.
    Journal of the Operational Research Society 06/2011; · 0.99 Impact Factor
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    N. Safaei, D. Banjevic, A. K. S. Jardine
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    ABSTRACT: In this paper, a real maintenance workforce-constrained scheduling problem is formulated as a bi-objective mixed-integer programming model with the aim of simultaneously minimizing the workforce requirements and maximizing the equipment availability. The skilled workforce is provided by internal and external resources using regular time, overtime and contracting. The equipment availability is measured by the downtime required for preventive maintenance (scheduled) and failure repair (unscheduled) jobs. We also encounter imminent or potential failures whose priorities depend on the severity of the failure on the system (secondary failure). The total weighted flow time is used as a scheduling criterion to measure the equipment availability; the weight of each job directly depends on the expected downtime resulting from the associated failure. The proposed model is verified using two comprehensive numerical examples and some sensitivity analyses. We conclude by discussing the results.
    Journal of the Operational Research Society 04/2011; · 0.99 Impact Factor
  • Ali Zuashkiani, Hazhir Rahmandad, Andrew K.S. Jardine
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    ABSTRACT: Purpose – The importance of physical assets has been increasingly recognized in recent decades. The significant returns on small improvements in overall equipment effectiveness (OEE) justify investment in the management of physical assets, but the wide variation of OEE across firms raises a question: “Why do these differences persist despite a high return on investments to maximize OEE?”. To address this question the dynamic processes that control the evolution of OEE through time need to be better understood. This paper aims to answer this question. Design/methodology/approach – Building on insights from system dynamics and strategy literature, the paper maps the reinforcing feedback loops governing the maintenance function and its interactions with various elements in a firm. Building on strategy literature it hypothesizes that these loops can explain wide variations in observed persistent variations in OEE among otherwise similar firms. The paper draws on previous literature, extensive case studies and consulting projects to provide such mapping using the qualitative mapping tools from system dynamics. Findings – The research outlines several reinforcing loops; once active, any of them could lead a firm towards a problematic mode of operation where reactive maintenance, poor morale, and a culture of fire-fighting dominate. Actions taken to fix problems in the short-run often activate vicious cycles, erode the capability of the organization over the long run, and lead to a lower OEE. Social implications – Knowing the factors affecting the asset management function of a plant increases the plant's safety and limits its environmental hazards. Originality/value – Some of the common dynamics of organizations' asset management practices are illustrated and modeled. The strategic importance of OEE and its effect on companies' market capitalization is demonstrated.
    Journal of Quality in Maintenance Engineering 03/2011; 17(1):74-92.
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    Sharareh Taghipour, Dragan Banjevic, Andrew K. S. Jardine
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    ABSTRACT: This paper proposes a method to analyze statistically maintenance data for complex medical devices with censoring and missing information. It presents a classification of different types of failures and establishes policies for analyzing data at system and component levels taking into account the failure types. The results of this analysis can be used as basic assumptions in development of a maintenance/inspection optimization model. As a case study, we present the reliability analysis of a general infusion pump from a hospital.
    Quality and Reliability Eng. Int. 01/2011; 27:71-84.
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    Tianqiao Tang, Daming Lin, Dragan Banjevic, Andrew K.S. Jardine
    Journal of Statistical Planning and Inference 01/2011; 141(1):610. · 0.71 Impact Factor
  • Nima Safaei, Dragan Banjevic, Andrew K. S. Jardine
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    ABSTRACT: The problem is related to a fleet of military aircraft with a certain flying program in which the availability of the aircraft sufficient to meet the flying program is a challenging issue. During the pre- or after-flight inspections, some component failures of the aircraft may be found. In such cases, the aircraft are sent to the repair shop to be scheduled for maintenance jobs, consisting of failure repairs or preventive maintenance tasks. The objective is to schedule the jobs in such a way that sufficient number of aircrafts is available for the next flight programs. The main resource, as well as the main constraint, in the shop is skilled-workforce. The problem is formulated as a mixed-integer mathematical programming model in which the network flow structure is used to simulate the flow of aircraft between missions, hanger and repair shop. The proposed model is solved using the classical Branch-and-Bound method and its performance is verified and analyzed in terms of a number of test problems adopted from the real data. The results empirically supported practical utility of the proposed model.
    Annals of Operations Research 01/2011; 186:295-316. · 1.03 Impact Factor
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    R. Pascual, D. Louit, Andrew K. S. Jardine
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    ABSTRACT: The introduction of International Standard IEC 61508 and its industry-specific derivatives sets demanding requirements for the definition and implementation of life-cycle strategies for safety systems. Compliance with the Standard is important for human safety and environmental perspectives as well as for potential adverse economic effects (eg, damage to critical downstream equipment or a clause for an insurance or warranty contract). This situation encourages the use of reliability models to attain the recommended safety integrity levels using credible assumptions. During the operation phase of the safety system life cycle, a key decision is the definition of an inspection programme, namely its frequency and the maintenance activities to be performed. These may vary from minimal checks to complete renewals. This work presents a model (which we called ρβ model) to find optimal inspection intervals for a safety system, considering that it degrades in time, even when it is inspected at regular intervals. Such situation occurs because most inspections are partial, that is, not all potential failure modes are observable through inspections. Possible reasons for this are the nature and the extent of the inspection, or potential risks generated by the inspection itself. The optimization criterion considered here is the mean overall availability A, but also taking into account the requirements for the safety availability A. We consider several conditions that ensure coherent modelling for these systems: sub-systems decomposition, k-out-of-n architectures, diagnostics coverage (observable/total amount of failure modes), dependent and independent failures, and non-negligible inspection times. The model requires an estimation for the coverage and dependent-failure ratios for each component, global failure rates, and inspection times. We illustrate its use through case studies and compare results with those obtained by applying previously published methodologies.
    Journal of the Operational Research Society 01/2011; 62:2051-2062. · 0.99 Impact Factor
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    Sharareh Taghipour, Dragan Banjevic, Andrew K. S. Jardine
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    ABSTRACT: 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 hospital's maintenance management system to illustrate an application of the proposed model.
    Journal of the Operational Research Society 01/2011; 62:1666-1687. · 0.99 Impact Factor
  • Nima Safaei, Dragan Banjevic, Andrew K. S. Jardine
    JORS. 01/2011; 62:1005-1018.
  • E.L. Wong, A.K.S. Jardine, D. Banjevic
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    ABSTRACT: Any organization that owns any large capital assets will eventually face a crucial decision - whether to repair or replace those assets, and when. This decision can have far-reaching consequences - replacing too early can mean a waste of resources, and replacing too late can mean catastrophic failure. The first is becoming more unacceptable in today's sustainability-oriented society, and the second is unacceptable in the competitive marketplace. If large capital assets are analyzed as repairable systems, additional significant information can be incorporated into maintenance optimization models. Examples of such systems are power transformers in the electricity industry and haul trucks in the mining industry, among many others. When these assets break down, but have not yet reached their end-of-life, they can be repaired and returned to operating condition. However, these repairs often reduce the remaining useful life (RUL) of the system. The RUL of a system is an important factor in decision making for capital assets. If a company can correctly predict the remaining useful life of a repairable system, they can estimate the cost of maintaining and repairing the system until that point, or they can evaluate the potential benefits of replacing the entire system at a prior point. Standard methods of predicting the RUL often use condition monitoring data that companies may obtain as part of their regular maintenance practices. However, they often ignore or minimize the importance of repair information. It is expected that including this type of information can greatly improve RUL predictions upon further analysis. A number of other factors that must be considered when making economic decisions based on RUL will also be discussed. In this paper, we consider a proportional hazards model that includes a covariate based on the repair and maintenance information available. A case study is based on data from a major Canadian utility for power transformers. The covariate is s hown to improve the fit of a hazard model developed using EXAKT software.
    Reliability and Maintainability Symposium (RAMS), 2011 Proceedings - Annual; 01/2011
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    ABSTRACT: It is widely accepted that one of the potential benefits of condition-based maintenance (CBM) is the expected decrease in inventory as the procurement of parts can be triggered by the identification of a potential failure. For this to be possible, the interval between the identification of the potential failure and the occurrence of a functional failure (P-F interval) needs to be longer than the lead time for the required part. In this paper we present a model directed to the determination of the ordering decision for a spare part when the component in operation is subject to a condition monitoring program. In our model the ordering decision depends on the remaining useful life (RUL) estimation obtained through (i) the assessment of component age and (ii) condition indicators (covariates) that are indicative of the state of health of the component, at every inspection time. We consider a random lead time for spares, and a single-component, single-spare configuration that is not uncommon for very expensive and highly critical equipment.
    Mechanical Systems and Signal Processing 01/2011; 25(5):1837-1848. · 1.91 Impact Factor