Towards an Open Standards-based Architecture for Condition-based Predictive Maintenance and IIoT

Tracking #: 2083-3296

This paper is currently under review
Authors: 
Karamjit Kaur
Matt Selway
Georg Grossman
Markus Stumptner
Alan Johnston
Pak Wong

Responsible editor: 
Guest Editors Sensors Observations 2018

Submission type: 
Full Paper
Abstract: 
In today's swiftly emerging Industry 4.0 environment, sensors/devices, machines and components have digital twins which are connected together enabling them to talk to each other. Factory operators can thus continuously capture machine states/condition and combine it with information from other systems, analyze it and predict the optimal point in time at which to initiate maintenance. This approach for maintenance generally called Condition-based Predictive Maintenance (CBPdM) can pinpoint imminent outages well before they occur, significantly enriching business performance by avoiding lengthy production outages. CBPdM is among the major focus points of the Industry 4.0 and IIoT. Interoperability of the asset management systems is crucial in achieving accurate diagnosis and prognosis as it can highly augment the data received from assets. The foundation of an interoperability architecture are standards. Unfortunately, even after wider adoption of CBPdM in industry, to the best of our knowledge there does not exist any reference architecture for it. This paper contributes by introducing Open Industrial Interoperability Ecosystem (OIIE) architecture which is an outgrowth of several related industry standardization activities for achieving standards-based interoperability. We illustrate how the architecture addresses the requirements of Industry 4.0 and CBPdM with the help of a detailed use case.
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