Collaborative Predictive Maintenance For Smart Manufacturing
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Collaborative Predictive Maintenance For Smart Manufacturing
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Author : Ali Bemani
language : en
Publisher:
Release Date : 2024
Collaborative Predictive Maintenance For Smart Manufacturing written by Ali Bemani and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.
Predictive Maintenance In Smart Factories
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Author : Tania Cerquitelli
language : en
Publisher: Springer Nature
Release Date : 2021-08-26
Predictive Maintenance In Smart Factories written by Tania Cerquitelli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-26 with Science categories.
This book presents the outcome of the European project "SERENA", involving fourteen partners as international academics, technological companies, and industrial factories, addressing the design and development of a plug-n-play end-to-end cloud architecture, and enabling predictive maintenance of industrial equipment to be easily exploitable by small and medium manufacturing companies with a very limited data analytics experience. Perspectives and new opportunities to address open issues on predictive maintenance conclude the book with some interesting suggestions of future research directions to continue the growth of the manufacturing intelligence.
Data Analytics And Artificial Intelligence For Predictive Maintenance In Smart Manufacturing
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Author : Amit Kumar Tyagi
language : en
Publisher:
Release Date : 2024-09-30
Data Analytics And Artificial Intelligence For Predictive Maintenance In Smart Manufacturing written by Amit Kumar Tyagi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-30 with Computers categories.
Data Analytics and Artificial Intelligence (AI) play an important role in Predictive Maintenance (PdM) within the manufacturing industry. This book contains up-to-date information on predictive maintenance and the latest advancements, trends, and tools required to reduce costs and save time for manufacturers and industries.
Advances In Production Management Systems Smart Manufacturing For Industry 4 0
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Author : Ilkyeong Moon
language : en
Publisher: Springer
Release Date : 2018-08-24
Advances In Production Management Systems Smart Manufacturing For Industry 4 0 written by Ilkyeong Moon and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-24 with Computers categories.
The two-volume set IFIP AICT 535 and 536 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2018, held in Seoul, South Korea, in August 2018. The 129 revised full papers presented were carefully reviewed and selected from 149 submissions. They are organized in the following topical sections: lean and green manufacturing; operations management in engineer-to-order manufacturing; product-service systems, customer-driven innovation and value co-creation; collaborative networks; smart production for mass customization; global supply chain management; knowledge based production planning and control; knowledge based engineering; intelligent diagnostics and maintenance solutions for smart manufacturing; service engineering based on smart manufacturing capabilities; smart city interoperability and cross-platform implementation; manufacturing performance management in smart factories; industry 4.0 - digital twin; industry 4.0 - smart factory; and industry 4.0 - collaborative cyber-physical production and human systems.
Intelligent Predictive Maintenance
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Author : Min Liu
language : en
Publisher: Springer Nature
Release Date :
Intelligent Predictive Maintenance written by Min Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Trends And Applications In Mechanical Engineering Composite Materials And Smart Manufacturing
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Author : Padhi, Surya Narayan
language : en
Publisher: IGI Global
Release Date : 2024-08-14
Trends And Applications In Mechanical Engineering Composite Materials And Smart Manufacturing written by Padhi, Surya Narayan and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-14 with Technology & Engineering categories.
The fields of Mechanical Engineering, Composite Materials, and Smart Manufacturing find themselves at the heart of a pivotal predicament. As these industries grapple with the demands for efficiency, sustainability, and innovation, a need arises for a unified exploration of the transformative solutions within these domains. At this crucial moment, researchers, academics, and practitioners worldwide need to focus on understanding and solving the complex issues that are hindering progress. Trends and Applications in Mechanical Engineering, Composite Materials and Smart Manufacturing delves into solutions that propel industries, economies, and societies into a future defined by progress and resilience. At its core, this book strives to examine the disciplines of mechanical engineering, composite materials, and smart manufacturing. With the collaborative efforts of diverse experts, it attempts to create a comprehensive resource that not only identifies emerging trends but also unveils their impact on the real world. By acting as a driving force for advancing current research, bridging knowledge gaps, and presenting innovative solutions, the publication contributes significantly to the collective understanding of these disciplines. The goal is to empower scholars, educators, and professionals with the knowledge and insights required to sculpt the future of these increasingly complex industries.
Service Oriented Holonic And Multi Agent Manufacturing Systems For Industry Of The Future
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Author : Theodor Borangiu
language : en
Publisher: Springer Nature
Release Date : 2021-03-02
Service Oriented Holonic And Multi Agent Manufacturing Systems For Industry Of The Future written by Theodor Borangiu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-02 with Technology & Engineering categories.
The scientific theme of the book concerns “Manufacturing as a Service (MaaS)” which is developed in a layered cloud networked manufacturing perspective, from the shop floor resource sharing model to the virtual enterprise collaborative model, by distributing the cost of the manufacturing infrastructure - equipment, software, maintenance, networking - across all customers. MaaS is approached in terms of new models of service-oriented, knowledge-based manufacturing systems optimized and reality-aware, that deliver value to customer and manufacturer via Big data analytics, Internet of Things communications, Machine learning and Digital twins embedded in Cyber-Physical System frameworks. From product design to after-sales services, MaaS relies on the servitization of manufacturing operations such as: Design as a Service, Predict as a Service or Maintain as a service. The general scope of the book is to foster innovation in smart and sustainable manufacturing and logistics systems and in this context to promote concepts, methods and solutions for the digital transformation of manufacturing through service orientation in holonic and agent-based control with distributed intelligence. The book’s readership is comprised by researchers and engineers working in the manufacturing value chain area who develop and use digital control solutions in the ‘Industry of the Future’ vision. The book also addresses to master and Ph.D. students enrolled in Engineering Sciences programs.
Predictive Maintenance In Dynamic Systems
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Author : Edwin Lughofer
language : en
Publisher: Springer
Release Date : 2019-02-28
Predictive Maintenance In Dynamic Systems written by Edwin Lughofer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-28 with Technology & Engineering categories.
This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet of Things. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power.
Intelligent Maintenance And Monitoring Strategy For Smart Manufacturing Systems
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Author : Honghan Ye
language : en
Publisher:
Release Date : 2021
Intelligent Maintenance And Monitoring Strategy For Smart Manufacturing Systems written by Honghan Ye and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
In smart manufacturing systems, production scheduling, maintenance decision-making, and process monitoring are three key, closely interconnected components, which play significant roles in the system performance, quality control, and overall cost. Due to the rapid advancement of in-process measurements and sensor technology, massive data frequently appear in modern industries. While such a data-rich environment has the potential to better reveal real-time details of the underlying system and make better decisions for the system improvement, it also presents significant challenges in the following perspectives: (i) how to effectively leverage the acquired knowledge to balance trade-offs between conflicting objectives, (ii) how to optimally design the monitoring system given the practical resources constraint, and (iii) how to efficiently handle the high-dimensional heterogeneous information with different acquisition rates, distributions, and characteristics. This thesis concentrates on production and maintenance scheduling, and process monitoring to develop systematic analytics methodologies for quality control, cost reduction, and performance improvement in smart manufacturing systems. By incorporating engineering domain knowledge with advanced statistical techniques, the proposed methodologies facilitate (i) the real-time decisions that improve the system production and maintenance scheduling, (ii) the effective monitoring of system status, (iii) the informative and intelligent decisions on balancing between exploration and exploitation given the limited monitoring resources, and (iv) the asynchronous process monitoring with different data acquisition rates. The first chapter introduces the background and challenges in production and maintenance scheduling, and monitoring in smart manufacturing systems, and establishes the major research objective of the thesis. Chapter 2 addresses a joint scheduling problem that considers corrective maintenance (CM) due to unexpected breakdowns and scheduled preventive maintenance (PM) in a generic M-machine flow shop. The objective is to find the optimal job sequence and PM schedule such that the total of tardiness cost, PM cost, and CM cost is minimized. To address this critical research issue, our novel idea is to dynamically update the PM interval based on real-time machine age, such that maintenance activity coordinates with job scheduling to the maximum extent, which results in an overall cost saving. With the rapid development of sensor technology, real-time observations from the sensors can be used to describe the machine status more accurately and achieve early anomaly detection. In Chapter 3, we propose a nonparametric monitoring and sampling algorithm integrated with Thompson sampling to quickly detect abnormalities occurring in heterogeneous data streams. In particular, a Bayesian approach is incorporated with an antirank-based cumulative sum (CUSUM) procedure to collectively estimate the underlying status of all data streams based on the partially observed data. Furthermore, an intelligent sampling strategy based on Thompson sampling (TS) algorithm is proposed to dynamically observe the informative data streams and balance between exploration and exploitation to facilitate quick anomaly detection. While the proposed method in Chapter 3 shows good performance in monitoring heterogeneous data streams, it heavily relies on the assumption that full historical in-control observations of all data streams are available offline, which does not always hold in practice. To address this issue, Chapter 4 further proposes a generic online nonparametric monitoring and sampling scheme occurring in high-dimensional heterogeneous processes when only partial observations are available. Specifically, we integrate the TS algorithm with a quantile-based nonparametric CUSUM procedure to construct local statistics of all data streams based on the partially observed data. Further, we develop a global monitoring scheme by using the sum of top-r local statistics to screen out the most suspicious data streams. Chapter 5 proposes a generic top - r based asynchronous monitoring (TRAM) framework to online monitor high-dimensional heterogeneous and asynchronous processes, where measurements of each data stream follow arbitrary distributions and are collected at different sampling intervals. In particular, we first adopt a quantile-based nonparametric CUSUM scheme to monitor each data stream locally. Then, an effective compensation strategy is proposed for unsampled data streams at the local statistics level to alleviate severe detection delay when mean shifts occur to long-sampling-interval data streams. Furthermore, we develop a global monitoring scheme using the sum of top - r local statistics, which is able to quickly detect a wide range of possible mean shifts in all directions. Chapter 6 then summarizes the contribution of the thesis. In summary, this thesis contributes to developing systematic analytics methodologies for quality control, cost reduction, and performance improvement in smart manufacturing systems. The developed methods are generic and can also be applied to other applications such as healthcare, energy and climate research, which will lead to improved maintenance scheduling, efficient resource allocation, and significant overall cost savings.
Manufacturing 4 0
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Author : O. Perez
language : en
Publisher: Palibrio
Release Date : 2018-08-07
Manufacturing 4 0 written by O. Perez and has been published by Palibrio this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-07 with Business & Economics categories.
Manufacturing 4.0 The Use of Emergent Technologies in Manufacturing This book provides a comprehensive framework to understand and use Industry 4.0 emergent technologies in manufacturing for the hands-on engineers. It details the contribution of Lean and Manufacturing 4.0 to reduce and handle the increasing complexity experienced in the production floor. In addition, it classifies manufacturing under three attributes describing the way each of them modify it: Digital, Automated, and Additive. Each of these modifiers is presented as a chapter with a strategy, a detail description of the set of tools around them, and examples to make it easy to understand for the reader. The hype of industry 4.0 and its derivative technologies inevitably creates new business models but it also significantly impacts key process indicators. The integration, and exploitation of a subset of Industry 4.0 technologies is baptized as manufacturing 4.0 in this book. The book also outlines a manufacturing 4.0 implementation Strategy as part of the continuous improvement journey to assess, outline solutions, evaluate the benefit and risk, review with stakeholders, and create a portfolio. A roadmap provides a guideline together with all the explanations of the different technology applications in order to use it as a reference. The goal is for you to apply these technology enablers on the right problems to benefit your organization.