Towards Predictive Maintenance for Flexible Manufacturing Using FIWARE
Authors: Sang, G.M., Xu, L., de Vrieze, P. and Bai, Y.
Journal: Lecture Notes in Business Information Processing
Volume: 382 LNBIP
Pages: 17-28
eISSN: 1865-1356
ISSN: 1865-1348
DOI: 10.1007/978-3-030-49165-9_2
Abstract:Industry 4.0 has shifted the manufacturing related processes from conventional processes within one organization to collaborative processes across different organizations. For example, product design processes, manufacturing processes, and maintenance processes across different factories and enterprises. This complex and competitive collaboration requires the underlying system architecture and platform to be flexible and extensible to support the demands of dynamic collaborations as well as advanced functionalities such as big data analytics. Both operation and condition of the production equipment are critical to the whole manufacturing process. Failures of any machine tools can easily have impact on the subsequent value-added processes of the collaboration. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related machineries using various analyses. In this context, this paper explores how the FIWARE framework supports predictive maintenance. Specifically, it looks at applying a data driven approach to the Long Short-Term Memory Network (LSTM) model for machine condition and remaining useful life to support predictive maintenance using FIWARE framework in a modular fashion.
https://eprints.bournemouth.ac.uk/34118/
Source: Scopus
Towards Predictive Maintenance for Flexible Manufacturing Using FIWARE
Authors: Sang, G.M., Xu, L., de Vrieze, P. and Bai, Y.
Journal: ADVANCED INFORMATION SYSTEMS ENGINEERING WORKSHOPS
Volume: 382
Pages: 17-28
eISSN: 1865-1356
ISBN: 978-3-030-49164-2
ISSN: 1865-1348
DOI: 10.1007/978-3-030-49165-9_2
https://eprints.bournemouth.ac.uk/34118/
Source: Web of Science (Lite)
Towards Predictive Maintenance for Flexible Manufacturing Using FIWARE
Authors: Sang, G.M., Xu, L., de Vrieze, P.T. and Bai, Y.
Conference: CAiSE 2020
Pages: 17-28
ISBN: 9783030491642
ISSN: 1865-1348
Abstract:Industry 4.0 has shifted the manufacturing related processes from conventional processes within one organization to collaborative processes across different organizations. For example, product design processes, manufacturing processes, and maintenance processes across different factories and enterprises. This complex and competitive collaboration requires the underlying system architecture and platform to be flexible and extensible to support the demands of dynamic collaborations as well as advanced functionalities such as big data analytics. Both operation and condition of the production equipment are critical to the whole manufacturing process. Failures of any machine tools can easily have impact on the subsequent value-added processes of the collaboration. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related machineries using various analyses. In this context, this paper explores how the FIWARE framework supports predictive maintenance. Specifically, it looks at applying a data driven approach to the Long Short-Term Memory Network (LSTM) model for machine condition and remaining useful life to support predictive maintenance using FIWARE framework in a modular fashion.
https://eprints.bournemouth.ac.uk/34118/
Source: BURO EPrints