Case study: the implementation of a data-driven industrial analytics methodology and platform for smart manufacturing

Peter O'Donovan, Ken Bruton, and Dominic T.J. O'Sullivan
Publication Target: 
IJPHM
Publication Issue: 
Special Issue on Smart Manufacturing PHM
Submission Type: 
Full Paper
Supporting Agencies (optional): 
DePuy Johnson & Johnson
AttachmentSizeTimestamp
ijphm_16_026.pdf1.63 MBOctober 4, 2016 - 10:27am

Integrated, real-time and open approaches relating to the development of industrial analytics capabilities are needed to support smart manufacturing. However, adopting industrial analytics can be challenging due to its multidisciplinary and cross-departmental (e.g. Operation and Information Technology) nature. These challenges stem from the significant effort needed to coordinate and manage teams and technologies in a connected enterprise. To address these challenges, this research presents a formal industrial analytics methodology that may be used to inform the development of industrial analytics capabilities. The methodology classifies operational teams that comprise the industrial analytics ecosystem, and presents a technology agnostic reference architecture to facilitate the industrial analytics lifecycle. Finally, the proposed methodology is demonstrated in a case study, where an industrial analytics platform is used to identify an operational issue in a large-scale Air Handling Unit (AHU).

Publication Year: 
2016
Publication Volume: 
7
Publication Control Number: 
026
Page Count: 
21
Submission Keywords: 
smart manufacturing
Big Data
energy efficiency
industrial analytics
air handling unit
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Industrial applications
Systems and platform applications
Submitted by: 
  
 
 
 

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