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215 results on '"Baglee David"'

1. Business Performance Measurements in Asset Management with the Support of Big Data Technologies

Author

Campos Jaime, Sharma Pankaj, Jantunen Erkki, Baglee David, and Fumagalli Luca

Subjects
business performance measurements, asset management, big data technologies, Production management. Operations management, TS155-194
Abstract

The paper reviews the performance measurement in the domain of interest. Important data in asset management are further, discussed. The importance and the characteristics of today’s ICTs capabilities are also mentioned in the paper. The role of new concepts such as big data and data mining analytical technologies in managing the performance measurements in asset management are discussed in detail. The authors consequently suggest the use of the modified Balanced Scorecard methodology highlighting both quantitative and qualitative aspects, which is crucial for optimal use of the big data approach and technologies.

Published
2017
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2. Condition Monitoring in the Advances of Industry 4.0—Case Study

Author

Shaalan, Abdu, Baglee, David, IFToMM, Series Editor, Ceccarelli, Marco, Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Agrawal, Sunil K., Advisory Editor, Ball, Andrew D., editor, Ouyang, Huajiang, editor, Sinha, Jyoti K., editor, and Wang, Zuolu, editor

Published
2024
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3. Technology and the Future of Maintenance

Author

Dixon, Derek, Baglee, David, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Kumar, Uday, editor, Karim, Ramin, editor, Galar, Diego, editor, and Kour, Ravdeep, editor

Published
2024
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8. Are We Ready for Industry 4.0?

Author

Shaalan, Abdu, Baglee, David, Knowles, Michael, Howlett, Robert J., Series Editor, Jain, Lakhmi C., Series Editor, Ball, Andrew, editor, Gelman, Len, editor, and Rao, B. K. N., editor

Published
2020
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9. Development of Modern Maintenance Management Strategy for Complex Manufacturing Assets

Author

Shaalan, Abdu, Baglee, David, Knowles, Michael, Marttonen-Arola, Salla, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Liyanage, Jayantha P., editor, Amadi-Echendu, Joe, editor, and Mathew, Joseph, editor

Published
2020
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10. The Use of Relational and NoSQL Databases in Industrial Asset Management

Author

Campos, Jaime, Sharma, Pankaj, Cañizo, Mikel, Jantunen, Erkki, Baglee, David, Charramendieta, Santiago, Conde, Angel, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Liyanage, Jayantha P., editor, Amadi-Echendu, Joe, editor, and Mathew, Joseph, editor

Published
2020
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11. Introducing Lean into Maintenance Data Management: A Decision Making Approach

Author

Marttonen-Arola, Salla, Baglee, David, Kinnunen, Sini-Kaisu, Holgado, Maria, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Liyanage, Jayantha P., editor, Amadi-Echendu, Joe, editor, and Mathew, Joseph, editor

Published
2020
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12. The Implementation of ISO 55000 in Small and Medium Enterprises: Requirements and Constraints

Author

Ihemegbulem, Ibifuro, Baglee, David, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Liyanage, Jayantha P., editor, Amadi-Echendu, Joe, editor, and Mathew, Joseph, editor

Published
2020
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14. Achieving Autonomous Compressive Spectrum Sensing for Cognitive Radios

Author

Jiang, Jing, Sun, Hongjian, Baglee, David, and Poor, H. Vincent

Subjects
Computer Science - Information Theory
Abstract

Compressive sensing (CS) technologies present many advantages over other existing approaches for implementing wideband spectrum sensing in cognitive radios (CRs), such as reduced sampling rate and computational complexity. However, there are two significant challenges: 1) choosing an appropriate number of sub-Nyquist measurements, and 2) deciding when to terminate the greedy recovery algorithm that reconstructs wideband spectrum. In this paper, an autonomous compressive spectrum sensing (ACSS) framework is presented that enables a CR to automatically choose the number of measurements while guaranteeing the wideband spectrum recovery with a small predictable recovery error. This is realized by the proposed measurement infrastructure and the validation technique. The proposed ACSS can find a good spectral estimate with high confidence by using only a small testing subset in both noiseless and noisy environments. Furthermore, a sparsity-aware spectral recovery algorithm is proposed to recover the wideband spectrum without requiring knowledge of the instantaneous spectral sparsity level. Such an algorithm bridges the gap between CS theory and practical spectrum sensing. Simulation results show that ACSS can not only recover the spectrum using an appropriate number of measurements, but can also considerably improve the spectral recovery performance compared with existing CS approaches. The proposed recovery algorithm can autonomously adopt a proper number of iterations, therefore solving the problems of under-fitting or over-fitting which commonly exist in most greedy recovery algorithms., Comment: This paper is accepted by IEEE Transactions on Vehicular Technology 2015. The detail will be updated soon

Published
2015
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40. A New Integrated E-maintenance Concept

Author

Arnaiz, Aitor, Iung, Benoit, Al-Najjar, Basim, Jantunen, Erkki, Holmberg, Kenneth, Naks, Tonu, Baglee, David, Holmberg, Kenneth, editor, Adgar, Adam, editor, Arnaiz, Aitor, editor, Jantunen, Erkki, editor, Mascolo, Julien, editor, and Mekid, Samir, editor

Published
2010
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42. Development of maintenance framework for modern manufacturing systems

Author

Shaalan, Abdu, Baglee, David, Dixon, Derek, and Shaalan, Abdu

Subjects
top_engineering
Abstract

Modern manufacturing organizations are designing, building and operating large, complex and often ‘one of a kind’ assets, which incorporate the integration of various systems under modern control systems. Due to such complexity, machines failures became more difficult to interpret and rectify and the existing maintenance strategies became obsolete without development and enhancement. As a result, the need for more advanced strategies to ensure effective maintenance applications that ensures high operation efficiency arise. The current research aims to investigate the existing maintenance strategies, the levels of machines complexity and automation within manufacturing companies from different sectors and sizes including, oil and gas, food and beverages, automotive, aerospace, and Original Equipment Manufacturer. Results analysis supports in the development of a modern maintenance framework that overcome the highlighted results and suits modern manufacturing assets using systematic approaches and utilisation of pillars from Total productive maintenance (TPM, Reliability Centred Maintenance (RCM) and Industry 4.0.

Published
2022

43. The development of CMMS incorporating condition monitoring tools in the advances of Industry 4

Author

shaalan, Abdu, Baglee, David, Morris, Adrian, and Shaalan, Abdu

Subjects
top_engineering
Abstract

Computerized maintenance management software (CMMS) considered effective supporting tools to enhance the organisation and scheduling practices of maintenance tasks on manufacturing assets. Condition monitoring applications in the advances of Industry 4.0 applications enhances machines condition insight by utilising different sensing nodes to improve the optimisation of the scheduled maintenance tasks and support predictive maintenance applications. To overcome the disconnection between condition monitoring technology and CMMS software, the research presents a new generation of CMMS by integrating condition monitoring technologies with maintenance management functionalities under a single cloud-based platform. As an example, energy data from five-axis machine tools are included to show it is predictable and stable to be reliable for failures prediction applications.

Published
2022

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