iFactory Cloud Service Platform Based on IMS Tools and Servo-lution

Kao, Ann; Lee, Jay; Lapira, Edzel; Yang, Shanhu; Huang, Yulei; Yen, Nelson

doi:10.1007/978-1-4471-4993-4_60

Cited by 4 publications

(4 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cloud based PHM leverages many existing technologies such as distributed computing, grid computing and virtualization and integrates them based on a Service-Oriented Architecture (SOA) [23]. Kao et al has presented a new platform for implementing cloudbased PHM infrastructure, which is called the iFactory [31]. Based on this platform, a cloud infrastructure and a set of PHM algorithms can be integrated to deliver the service to factory users via a graphical user interface.…”

Section: Cloud-based Phm Deployment Platformmentioning

confidence: 99%

“…Based on this platform, a cloud infrastructure and a set of PHM algorithms can be integrated to deliver the service to factory users via a graphical user interface. An infrastructure of cloud manufacturing has already been developed for machine tool builders based on three levels: a manufacturing service channel, an in-factory resource management, and an intelligent service platform [31]. However, since the cloud-based PHM system targets at multiple machines in various working conditions (which can be geographically distributed), the PHM workflows need to be highly flexible and self-adaptive for different monitoring cases.…”

Section: Cloud-based Phm Deployment Platformmentioning

confidence: 99%

See 1 more Smart Citation

Deployment of Prognostics Technologies and Tools for Asset Management: Platforms and Applications

Lee

Ardakani

Kao

et al. 2015

Engineering Asset Management Review

View full text Add to dashboard Cite

Over recent years, a significant amount of research has been dedicated to the development of Prognostics and Health Management (PHM) models. However, less attention is paid towards implementing the developed models to real-world applications to serve the needs of industry. In order to successfully implement a PHM system, a systematic approach is required to deploy the developed analytic tools (algorithms, software and agents) using a scalable hardware platform. In this paper, different PHM deployment platforms including stand-alone PC, embedded and cloud-based platforms are benchmarked. Then, a unified strategy for deploying the developed PHM tools using each of these platforms is presented. A smart deployment platform selection method using Quality Function Deployment (QFD) is also introduced. Following that, several case studies from different applications are provided as examples to demonstrate the capabilities and limitations of each deployment platform.

show abstract

Section: Cloud-based Phm Deployment Platformmentioning

confidence: 99%

Section: Cloud-based Phm Deployment Platformmentioning

confidence: 99%

Deployment of Prognostics Technologies and Tools for Asset Management: Platforms and Applications

Lee

Ardakani

Kao

et al. 2015

Engineering Asset Management Review

View full text Add to dashboard Cite

show abstract

“…To acquire the operation condition from machines, frameworks for extracting controller signals from CNC systems have been designed and demonstrated in previous research (Kao et al, 2014). In a real-time machine condition monitoring subsystem, a controller signal extractor and real-time monitoring module are integrated in an industrial PC.…”

Section: Application Overviewmentioning

confidence: 99%

“…Therefore, IT infrastructures have been developed to support the computational and network needs. An example of such a cloud-based PHM system is that for the machine tool builders provided in Kao et al (2014). Cloud computing is being used to provide a more secure and easy option for the storage of large-scale data files and improves the connectivity of services and data sets (Chun and Maniatis, 2009).…”

Section: Industrial Agent Platforms Based On Cloud Infrastructuresmentioning

confidence: 99%