328 research outputs found

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    IEOM Society International

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    IEOM Society Internationa

    A Bayesian Network Approach for Product Safety Risk Management

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    A new method for safety risk management and assessment using Bayesian networks is proposed to resolve limitations of existing methods and to ensure that products and systems available on the market are acceptably safe for use. The method is applicable to a wide range of products and systems, ranging from consumer goods through to medical devices, and even complex systems such as aircraft. While methods such as Fault Tree Analysis (FTA) and Failure Mode and Effects Analysis (FMEA) have been used quite effectively in safety assessment for certain classes of critical systems, they have several limitations which are addressed by the proposed Bayesian network (BN) method. In particular, the BN approach enables us to combine multiple sources of knowledge and data to provide quantified, auditable risk estimates at all stages of a product’s life cycle, including especially when there are limited or no testing or operational safety data available. The BN approach also enables us to incorporate different perceptions of risk, including taking account of personal differences in the perceived benefits of the product under assessment. The proposed BN approach provides a means for safety regulators, manufacturers, risk professionals, and even individuals to better assess safety and risk. It is powerful and flexible, can complement traditional safety and risk assessment methods, and is applicable to a far greater range of products and systems. The method can also be used to validate the results of traditional safety and risk assessment methods when relevant data become available. It is demonstrated and validated using case studies from consumer product safety risk assessment and medical device risk management

    General Course Catalog [2022/23 academic year]

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    General Course Catalog, 2022/23 academic yearhttps://repository.stcloudstate.edu/undergencat/1134/thumbnail.jp

    Sustainable supplier selection based on industry 4.0 initiatives within the context of circular economy implementation in supply chain operations

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    This study proposes a decision framework based on industry 4.0 initiatives within circular economy implementation to evaluate and select sustainable suppliers. In this context, sustainable supplier selection, industry 4.0, and circular economy have emerged as key topics of the contemporary operations management debate. The mix method approach of combining literature review and industrial expert’s inputs was adopted to identify four main categories and twenty-one sub-categories relevant to the supplier selection decision. A multi-criteria decision-making support tool composed of the ‘best-worst method’ (BWM) and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) was applied to aid in the evaluation and selection of a sustainable supplier in Pakistan’s textile manufacturing company. The BWM approach was first applied to determine the relative importance weights, and then, VIKOR used to rank the suppliers. The findings of the study suggest that, the Pakistan’s textile manufacturing company places much emphasis and importance on ‘Technological and Infrastructure (TI)’ with weight of 0.356 and ‘a positive organizational culture towards implementation of industry 4.0 and circular economy initiatives’ (OG3) with global weight of 0.139 when embarking on such decisions, and ranked supplier 2 as the top sustainable supplier. Managerial and post-selection benchmarking negotiations and future research directions are also introduced

    Assessment, Diagnosis and Service Life Prediction

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    Service life prediction is crucial for the adoption of more sustainable solutions, allowing developers to optimize the costs and environmental impact of buildings during their life cycle. An accurate assessment of the service life of buildings requires a thorough understanding of the degradation mechanisms and behaviour of the construction materials. Building pathology assessment methods characterize the deterioration state of buildings, using specific measurable properties as indicators. Based on this information, different service life prediction methodologies can be defined to provide reliable data concerning the most probable failure time of whole buildings and individual components according to their characteristics and their age. This Special Issue provides new perspectives on the existing knowledge related with various aspects of the Assessment, Diagnosis and Service Life Prediction of buildings and their components. The ten original research studies published in this Special Issue result from research centres and university departments of Civil and Construction Engineering, Safety Management, Environmental Engineering, Geotechnical Engineering, and Architecture and the Built Environment, with relevant contributions from experts from Australia, Brazil, the Czech Republic, Hong Kong, Iran, Israel, Norway, Portugal, and Taiwan. The studies included in this Special Issue address topics related to: Building pathology assessment methods; Diagnosis of defects in buildings and components; Appropriate intervention and repair techniques; Deterministic and stochastic service life prediction models

    A Digital Triplet for Utilizing Offline Environments to Train Condition Monitoring Systems for Rolling Element Bearings

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    Manufacturing competitiveness is related to making a quality product while incurring the lowest costs. Unexpected downtime caused by equipment failure negatively impacts manufacturing competitiveness due to the ensuing defects and delays caused by the downtime. Manufacturers have adopted condition monitoring (CM) techniques to reduce unexpected downtime to augment maintenance strategies. The CM adoption has transitioned maintenance from Breakdown Maintenance (BM) to Condition-Based Maintenance (CbM) to anticipate impending failures and provide maintenance actions before equipment failure. CbM is the umbrella term for maintenance strategies that use condition monitoring techniques such as Preventive Maintenance (PM) and Predictive Maintenance (PdM). Preventive Maintenance involves providing periodic checks based on either time or sensory input. Predictive Maintenance utilizes continuous or periodic sensory inputs to determine the machine health state to predict the equipment failure. The overall goal of the work is to improve bearing diagnostic and prognostic predictions for equipment health by utilizing surrogate systems to generate failure data that represents production equipment failure, thereby providing training data for condition monitoring solutions without waiting for real world failure data. This research seeks to address the challenges of obtaining failure data for CM systems by incorporating a third system into monitoring strategies to create a Digital Triplet (DTr) for condition monitoring to increase the amount of possible data for condition monitoring. Bearings are a critical component in rotational manufacturing systems with wide application to other industries outside of manufacturing, such as energy and defense. The reinvented DTr system considers three components: the physical, surrogate, and digital systems. The physical system represents the real-world application in production that cannot fail. The surrogate system represents a physical component in a test system in an offline environment where data is generated to fill in gaps from data unavailable in the real-world system. The digital system is the CM system, which provides maintenance recommendations based on the ingested data from the real world and surrogate systems. In pursuing the research goal, a comprehensive bearing dataset detailing these four failure modes over different collection operating parameters was created. Subsequently, the collections occurred under different operating conditions, such as speed-varying, load-varying, and steadystate. Different frequency and time measures were used to analyze and identify differentiating criteria between the different failure classes over the differing operating conditions. These empirical observations were recreated using simulations to filter out potential outliers. The outputs of the physical model were combined with knowledge from the empirical observations to create ”spectral deltas” to augment existing bearing data and create new failure data that resemble similar frequency criteria to the original data. The primary verification occurred on a laboratory-bearing test stand. A conjecture is provided on how to scale to a larger system by analyzing a larger system from a local manufacturer. From the subsequent analysis of machine learning diagnosis and prognosis models, the original and augmented bearing data can complement each other during model training. The subsequent data substitution verifies that bearing data collected under different operating conditions and sizes can be substituted between different systems. Ostensibly, the full formulation of the digital triplet system is that bearing data generated at a smaller size can be scaled to train predictive failure models for larger bearing sizes. Future work should consider implementing this method for other systems outside of bearings, such as gears, non-rotational equipment, such as pumps, or even larger complex systems, such as computer numerically controlled machine tools or car engines. In addition, the method and process should not be restricted to only mechanical systems and could be applied to electrical systems, such as batteries. Furthermore, an investigation should consider further data-driven approximations to specific bearing characteristics related to the stiffness and damping parameters needed in modeling. A final consideration is for further investigation into the scalability quantities within the data and how to track these changes through different system levels
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