TY  - GEN
N2  - Royal Navy engineers are faced with the demanding responsibility of maintaining critical equipment to have high levels of reliability, availability, and performance under tight budget constraints. To avoid operating surprises, accurate assessment of equipment operating performance is needed to judge whether mission demands can be satisfied while maintenance costs are controlled.  

Large volumes of data about the health of complex system elements are generally available, and the amount of data is growing steadily. However, pulling together large amounts of current data from diverse sources across a system or an enterprise to create actionable intelligence is a challenge to the organization, from the plant engineer to the chief information officer.

The foregoing requirements need to be carefully considered for effective naval asset performance management (APM). This paper considers the use of digital systems on naval platforms for APM to create a purposeful, predictive-analytic solution. It examines both the technology and engineering challenges and demonstrates how data analytics techniques are being used with successful outcomes.  

One solution is to use Similarity-Based Modelling (SBM) within an APM system as the technology foundation for development of predictive-analytic solutions to a broad spectrum of real-time modelling needs. Such a development, outlined in this paper, has focused on providing a platform-wide, equipment-agnostic industrial solution that can meet the needs of challenging naval applications and proven commercially in the energy and marine industries.  

The paper also considers, from a naval OEM perspective, the Maritime Support and the Exploitation Strategy which enables, equips and empowers the Support Enterprise through Digital Transformation. 
AB  - Royal Navy engineers are faced with the demanding responsibility of maintaining critical equipment to have high levels of reliability, availability, and performance under tight budget constraints. To avoid operating surprises, accurate assessment of equipment operating performance is needed to judge whether mission demands can be satisfied while maintenance costs are controlled.  

Large volumes of data about the health of complex system elements are generally available, and the amount of data is growing steadily. However, pulling together large amounts of current data from diverse sources across a system or an enterprise to create actionable intelligence is a challenge to the organization, from the plant engineer to the chief information officer.

The foregoing requirements need to be carefully considered for effective naval asset performance management (APM). This paper considers the use of digital systems on naval platforms for APM to create a purposeful, predictive-analytic solution. It examines both the technology and engineering challenges and demonstrates how data analytics techniques are being used with successful outcomes.  

One solution is to use Similarity-Based Modelling (SBM) within an APM system as the technology foundation for development of predictive-analytic solutions to a broad spectrum of real-time modelling needs. Such a development, outlined in this paper, has focused on providing a platform-wide, equipment-agnostic industrial solution that can meet the needs of challenging naval applications and proven commercially in the energy and marine industries.  

The paper also considers, from a naval OEM perspective, the Maritime Support and the Exploitation Strategy which enables, equips and empowers the Support Enterprise through Digital Transformation. 
AD  - GE Energy Power Conversion UK Ltd, Rugby, England.  © GE 2018.
T1  - Digital - Benefits for Naval Platforms
DA  - 2018-10-04
AU  - Chaderton, D R
L1  - https://library.imarest.org/record/7629/files/INEC%202018%20Paper%20096%20Chaderton%20FINAL.pdf
JF  - Conference Proceedings of INEC
VL  - INEC 2018
PY  - 2018-10-04
ID  - 7629
L4  - https://library.imarest.org/record/7629/files/INEC%202018%20Paper%20096%20Chaderton%20FINAL.pdf
TI  - Digital - Benefits for Naval Platforms
Y1  - 2018-10-04
L2  - https://library.imarest.org/record/7629/files/INEC%202018%20Paper%20096%20Chaderton%20FINAL.pdf
LK  - https://imarest.org/inec
LK  - https://library.imarest.org/record/7629/files/INEC%202018%20Paper%20096%20Chaderton%20FINAL.pdf
UR  - https://imarest.org/inec
UR  - https://library.imarest.org/record/7629/files/INEC%202018%20Paper%20096%20Chaderton%20FINAL.pdf
ER  -