[Show abstract][Hide abstract] ABSTRACT: The new technologies for maintenance can vastly improve safety and efficiency. E-Maintenance is a technology that can aggregate data from multiple databases. Internet of Things (IoT) is defined as a novel paradigms to enable “things” (any object with a computational device able of communicating) to communicate, exchanging data with each other. IoT can detect and read the environment and equipments in real time. These advances in technology can result in work practice improvements for maintenance technicians if they are well applied.
To bring these technologies to technicians, a maintenance system needs to consider the data they actually need to perform better in their current context. The system’s User Interface needs to be goal-driven, providing the right information at the right time.
This paper explores the challenge of delivering knowledge to technicians in a way that improves their efficiency and safety. It introduces a conceptual framework to Situation Awareness (SA) that can be applied in designing systems for maintenance technicians. This framework defines seven entities of importance (personal, procedure, equipments, system, environment, team and enterprise) in maintenance work and how they affect the user's SA. The objective is to fill the current knowledge gap in studies of SA and assist the process of understanding data and situations of maintenance activities.
[Show abstract][Hide abstract] ABSTRACT: In Maintenance, a vital activity in industry, ineffective Situation Awareness (SAW) is responsible for 13-17% of accidents. Therefore, a successful SAW has the potential of leveraging safety and efficiency, especially in field work. Bibliographic revision shows that the main gap to develop this idea is the conflict between the structure of Maintenance (procedural) and that of SAW (dynamic). Using a holistic study to elaborate a conclusive definition of SAW in the field, our work was able to solve this dichotomy of structures by developing a conceptual framework which maintains the task-oriented nature of field operation while clustering SAW inputs into entities to outline their potential effects. This Conceptual Framework of Situation Awareness in Maintenance (CFSAM) acknowledges the link between SAW and UIs, and creates a definitive list of multiple factors/entities that supports responsiveness and improves reliability and resilience. The seven entities identified are: task, equipment, system, environment, team, enterprise and personal. To each of these entities, CFSAM assigns a role in the UI design, and analyses challenges and solutions. This new approach of considering SAW-oriented design is a multidisciplinary effort and so far the results are promising: it facilitates an efficient design of SAW in maintenance field work, increases the focus on safety and efficiency and leverages the potential of developing a coherent system with high level of adherence.
[Show abstract][Hide abstract] ABSTRACT: This article considers the problem of managing the risks associated with random equipment failures by optimizing decisions regarding the quantity and placement of critical spares over a network of related industrial sites. To develop the model and provide a practical example, we focus on the allocation of electrical transformer spares for a large-scale industrial producer, such as a mining company or chemical manufacturer, operating several different sites across a geographic region. In particular, we consider the risk of financial loss due to interrupted business and lost production following an unexpected transformer failure. A two-stage stochastic integer programming model with a conditional value-at-risk (CVaR) criterion to incorporate risk aversion is developed. Computational results are presented to illustrate the advantages of the CVaR approach compared to a corresponding expected cost minimization approach. The CVaR model results in policies that have lower loss than the corresponding risk neutral model since, at sufficiently high risk aversion levels, the CVaR model introduces the acquisition of more spares as a hedge against catastrophic scenarios.
The Engineering Economist 05/2014; 59(2). DOI:10.1080/0013791X.2013.876795 · 0.84 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Numerous mathematical models and software solutions have been developed to optimize spare parts (components) inventory based on the cost of holding versus the cost of downtime-with allowances for repairable and non-repairable components. Louit's model optimized spare parts selection for both repairable and non-repairable slow-moving spare parts and included several case studies but did not address how shelf-life affected the spare parts requirements. The model developed in this paper addresses the effects of spare parts shelf-life on the optimization of spare parts selection. Certain conditions exacerbate the deterioration of spare parts, thereby affecting the reliability of the supported system. This is especially evident in non-repairable components that are stored for extended periods.
2014 Annual Reliability and Maintainability Symposium (RAMS); 01/2014
[Show abstract][Hide abstract] ABSTRACT: While controversies regarding optimal breast cancer screening modalities, screening start and end ages, and screening frequencies continue to exist, additional population-based randomized trials are unlikely to be initiated to examine these concerns. Simulation models have been used to evaluate the efficacy and effectiveness of various breast cancer screening strategies, however these models were all developed using US data. Currently, there is a need to examine the optimal screening and treatment policies in the Canadian context. In this commentary, we discuss the current controversies pertaining to breast cancer screening, and describe the fundamental components of a simulation model, which can be used to inform breast cancer screening and treatment policies.
Canadian journal of public health. Revue canadienne de santé publique 04/2013; 103(6):e417-9. · 1.02 Impact Factor
[Show abstract][Hide abstract] 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.
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.
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.
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 01/2013; 108(3). DOI:10.1038/bjc.2012.596 · 4.84 Impact Factor
[Show abstract][Hide abstract] 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
[Show abstract][Hide abstract] 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. DOI:10.1016/j.jspi.2012.05.011 · 0.68 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In general the objective of E-Maintenance is to organize, structure and link the Information and Communication Technologies (ICT) to develop a maintenance support system that is effective and efficient during the whole life cycle of the product. Among the many challenges of E-Maintenance, human centered solutions that can better assist technicians and engineers are emerging, using ICT resources integrated with visualization technologies, such as Augmented Reality (AR). Supported by a human centered infrastructure, AR can bring knowledge to the real physical world, to assist the technician perform his/her work without needing to interrupt to consult manuals or electronic systems for information, real time data and safety issues. This work reviews solutions to create a human centered E-Maintenance and discusses the steps to implement this vision.
Chemical Engineering Transactions 01/2013; 33:385-390. DOI:10.3303/CET1333065 · 1.03 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Background
Evaluating 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.
We 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.
We 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.
For proper estimation of absolute risk of the main event of interest common risk factors associated with competing events should be identified and considered.
Breast Cancer Research and Treatment 06/2012; 134(2):839-51. DOI:10.1007/s10549-012-2113-6 · 3.94 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Purpose ‐ The ongoing availability of existing industrial systems/machines depends to a great extent on the form and level of product support. Product support, or the after sale service of a product, is important because it assures the expected function of the product in its operational phase. Product support is affected by a number of factors, including system reliability and maintainability characteristics and the operating environment. The purpose of this paper is to analyze the influence of time independent external factors of industrial systems on product support requirements and spare parts need. Design/methodology/approach ‐ This paper, after discussing the factors influencing product support, describes a method to estimate spare part requirements based on estimation of the actual reliability of a product under the influence of the product-operating environment using a proportional hazard model. A spare parts estimation software, Spare Management Software (SMS), is used to check the results. Then a case study addresses the management of the spare parts inventory based on the geographical location and required performance of the product. Findings ‐ The lack of good support and critical spare parts can cause the untimely stoppage of a machine/system. The forecasting of product support and spare parts requirements based on the reliability and maintainability characteristics of systems/components, along with influencing environmental factors, is one of the most effective strategies for preventing unplanned stoppages. The operating environment of a system/machine has a considerable influence on the performance of the system and its technical characteristics, such as its reliability, maintainability, and, consequently, availability. Therefore, the system operating environment should be considered when the required support and spare parts estimation is under review. Research limitations/implications ‐ In this research, the focus is on the estimation of the number of spare parts required. Only non-repairable components/parts in repairable systems are studied. In other words, the paper considers one-component systems or a single component within a larger system. The operation and maintenance phases are dealt with in the study, along with the external operating environment and time independent influencing factors. Practical implications ‐ The introduced method for spare parts estimation will enable management to improve system availability and production line efficiency while minimizing total production costs. Consequently, the plant life cycle cost will be minimized by releasing the tied-up costs incurred when stocking extra parts for a long time. Originality/value ‐ The paper provides a new outlook on product support and spare parts forecasting by taking the actual system operating environment into consideration. It helps managers and engineers to be realistic and act pragmatically while running and analyzing technical/industrial systems.
International Journal of Quality & Reliability Management 04/2012; 29(4). DOI:10.1108/02656711211224875
[Show abstract][Hide abstract] 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
[Show abstract][Hide abstract] 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 02/2012; 27(1):442-449. DOI:10.1109/TPWRS.2011.2166090 · 2.81 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This paper gives a brief background to the optimization of condition based maintenance (CBM) decisions, through proportional hazards modeling. It then shows how risk factors for breast cancer and its competing mortalities can be similar to condition monitoring variables and be used as predictors in a risk model.
Condition Monitoring of Machinery in Non-Stationary Operations, 01/2012: pages 395-401; , ISBN: 978-3-642-28767-1
[Show abstract][Hide abstract] 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 12/2011; 62(12):2051-2062. DOI:10.1057/jors.2010.173 · 0.95 Impact Factor