Introduction to formal concept analysis and its applications in reliability engineering

Rocco, Claudio M.; Hernández-Perdomo, Elvis; Mun, Johnathan

doi:10.1016/j.ress.2020.107002

Cited by 17 publications

(14 citation statements)

References 29 publications

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“…The FCA method used in this study is also used as a cluster analysis method across many industries [19]. In the study of gene expression, FCA can be used to develop an efficient algorithm to find biologically important genes with a negative correlation [42].…”

Section: Discussionmentioning

confidence: 99%

“…As mentioned above, an essential requirement for the analysis is that there can be overlapping groups of patients characterised by agreement in the factors studied. A traditional method that can detect overlapping combinations of binary factors and overlapping groups is formal concept analysis (FCA), which results in a visual structure describing overlapping groups (clusters), the so-called 'concept lattice' [18][19][20]. The use of binary factors and a concept lattice may seem to be limiting factors in this approach, but this is not the case.…”

Section: Each Combination Of Selected Factors Defines a Groupmentioning

confidence: 99%

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A theoretical model of health management using data-driven decision-making: the future of precision medicine and health

et al. 2021

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Background The burden of chronic and societal diseases is affected by many risk factors that can change over time. The minimalisation of disease-associated risk factors may contribute to long-term health. Therefore, new data-driven health management should be used in clinical decision-making in order to minimise future individual risks of disease and adverse health effects. Methods We aimed to develop a health trajectories (HT) management methodology based on electronic health records (EHR) and analysing overlapping groups of patients who share a similar risk of developing a particular disease or experiencing specific adverse health effects. Formal concept analysis (FCA) was applied to identify and visualise overlapping patient groups, as well as for decision-making. To demonstrate its capabilities, the theoretical model presented uses genuine data from a local total knee arthroplasty (TKA) register (a total of 1885 patients) and shows the influence of step by step changes in five lifestyle factors (BMI, smoking, activity, sports and long-distance walking) on the risk of early reoperation after TKA. Results The theoretical model of HT management demonstrates the potential of using EHR data to make data-driven recommendations to support both patients’ and physicians’ decision-making. The model example developed from the TKA register acts as a clinical decision-making tool, built to show surgeons and patients the likelihood of early reoperation after TKA and how the likelihood changes when factors are modified. The presented data-driven tool suits an individualised approach to health management because it quantifies the impact of various combinations of factors on the early reoperation rate after TKA and shows alternative combinations of factors that may change the reoperation risk. Conclusion This theoretical model introduces future HT management as an understandable way of conceiving patients’ futures with a view to positively (or negatively) changing their behaviour. The model’s ability to influence beneficial health care decision-making to improve patient outcomes should be proved using various real-world data from EHR datasets.

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Section: Discussionmentioning

confidence: 99%

Section: Each Combination Of Selected Factors Defines a Groupmentioning

confidence: 99%

A theoretical model of health management using data-driven decision-making: the future of precision medicine and health

et al. 2021

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“…The numerous considerations such as demand for a quality product at cost-conscious value, competition, exploration of new technology make reliability more critically during their design [1]. Recent advancements reveal an incredible improvement in the use of reliability engineering due to more intricate and advanced systems, which cater to the needs of the customer-driven market inefficient manner [2]. The reliability engineering was categorized into the specialized area.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness Improvement in Manufacturing Industry; Trilogy Study and Open Innovation Dynamics

Tayal

Kalsi

Gupta

et al. 2021

Journal of Open Innovation: Technology, Market, and Complexity

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The purpose of this investigation is to compute overall equipment effectiveness (OEE) in the small-scale industry. The novel approach is introduced to detect bottlenecks by which OEE can be improved. This study attempts to help small-medium enterprises in analyzing performance in a better way. The automotive industry was chosen for conducting the research. The present study is comprised of three phases. In the first phase, OEE was computed and compared with world-class manufacturing. The second phase included three-level of Pareto analysis followed by making fishbone diagram to mitigate the losses. The third phase conducted improved OEE in the industry. There are seven major losses present in the industry that adversely affect the effectiveness of machine in any industry. This approach can reduce these losses and improve the quality, asset utilization (AU), OEE, total effective equipment performance (TEEP) and productivity of the machine. The study exposes that Pareto analysis uncovers all the losses and works on the principle of 80/20 rule. The major losses were thoroughly explored with the help of the fishbone diagram and solutions were implemented at the shop floor. As a result, availability, performance, quality, OEE, AU, and TEPP show improvements by 4.6%, 8.06%, 6.66%, 16.23%, 4.16%, and 14.58%, respectively. The approach offers a good opportunity for both researchers and small-medium enterprises around the world to analyze the indicators of production losses, performance, and productivity in the manufacturing industry.

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“…These classical methods, however, may fall short when it comes to exhaustive analyses, as they are timeconsuming and often require a considerable manual effort Kabir (2017). To circumvent this challenge, Rocco et al Rocco et al (2020) propose the application of the formal analysis of concept, which is a mathematical method for data analysis that focuses on the relationship between sets of objects and attributes. This method, though, is rather restricted for exhaustive failure analysis of complex interconnected systems.…”

Section: Introductionmentioning

confidence: 99%

Identifying Critical Failure-Propagation in Function Models of Complex Systems

Guillouet¹,

Torres²

2021

Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)

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Complex interconnected systems have high demands on meaningful analysis of the impact of failures on the actual service provision. This includes the study of obvious and high probable events, but also failures that are difficult to anticipate, e.g. due to cascading effects or combined events. This work introduces a framework for failure analysis that enables the exhaustive identification of combined failures with the strongest impact on the functionality of a system. The framework consists of two principal elements: a method for capturing the propagation of failures in complex systems that are represented via function models, and algorithms for solving the identification problem, which is formulated as combinatorial optimization problem. The feasibility of the approach is verified at hand of a function model of an Offshore Wind Farm (OWF). Both algorithms are then applied to the model of an offshore wind farm in order to identify the failure combinations with the strongest impact on the functionality.

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Introduction to formal concept analysis and its applications in reliability engineering

Cited by 17 publications

References 29 publications

A theoretical model of health management using data-driven decision-making: the future of precision medicine and health

A theoretical model of health management using data-driven decision-making: the future of precision medicine and health

Effectiveness Improvement in Manufacturing Industry; Trilogy Study and Open Innovation Dynamics

Identifying Critical Failure-Propagation in Function Models of Complex Systems

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