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Open AccessArticle

An Industrial Digitalization Platform for Condition Monitoring and Predictive Maintenance of Pumping Equipment

1
School of Science, Engineering and Design, Teesside University, Middlesbrough TS1 3BA, UK
2
Scottish & Southern Energy Ltd., Knottingley, West Yorkshire WF11 8SQ, UK
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(17), 3781; https://doi.org/10.3390/s19173781
Received: 12 June 2019 / Revised: 22 August 2019 / Accepted: 28 August 2019 / Published: 31 August 2019
(This article belongs to the Special Issue Edge/Fog/Cloud Computing in the Internet of Things)
This paper is concerned with the implementation and field-testing of an edge device for real-time condition monitoring and fault detection for large-scale rotating equipment in the UK water industry. The edge device implements a local digital twin, processing information from low-cost transducers mounted on the equipment in real-time. Condition monitoring is achieved with sliding-mode observers employed as soft sensors to estimate critical internal pump parameters to help detect equipment wear before damage occurs. The paper describes the implementation of the edge system on a prototype microcontroller-based embedded platform, which supports the Modbus protocol; IP/GSM communication gateways provide remote connectivity to the network core, allowing further detailed analytics for predictive maintenance to take place. The paper first describes validation testing of the edge device using Hardware-In-The-Loop techniques, followed by trials on large-scale pumping equipment in the field. The paper concludes that the proposed system potentially delivers a flexible and low-cost industrial digitalization platform for condition monitoring and predictive maintenance applications in the water industry. View Full-Text
Keywords: industrial digitalization; industry 4.0; sliding mode observers; soft sensors; edge computing; condition monitoring; predictive maintenance; field testing; IoT industrial digitalization; industry 4.0; sliding mode observers; soft sensors; edge computing; condition monitoring; predictive maintenance; field testing; IoT
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MDPI and ACS Style

Short, M.; Twiddle, J. An Industrial Digitalization Platform for Condition Monitoring and Predictive Maintenance of Pumping Equipment. Sensors 2019, 19, 3781. https://doi.org/10.3390/s19173781

AMA Style

Short M, Twiddle J. An Industrial Digitalization Platform for Condition Monitoring and Predictive Maintenance of Pumping Equipment. Sensors. 2019; 19(17):3781. https://doi.org/10.3390/s19173781

Chicago/Turabian Style

Short, Michael; Twiddle, John. 2019. "An Industrial Digitalization Platform for Condition Monitoring and Predictive Maintenance of Pumping Equipment" Sensors 19, no. 17: 3781. https://doi.org/10.3390/s19173781

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