Abstract
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.
Highlights
With improved connectivity and dramatically increased access to low-cost computational power, many industries are currently on the verge of a second digital revolution known as ‘Industry 4.00 [1].Of the many advantages that can potentially be leveraged by Industry 4.0, the promise of increased integration of the real-time control, monitoring, and operational aspects of equipment in the process industries is one of the most attractive [1,2]
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
Since it is essential to have adequate fault indices that could serve as indicators in order to predict early failures and create an effective Predictive Maintenance (PM) schedule [2], in an Industry 4.0 context an appropriate approach would seem to be to distribute the tasks of Condition Monitoring (CM)/indices generation and PM analytics between the network edges and Core
Summary
With improved connectivity and dramatically increased access to low-cost computational power, many industries are currently on the verge of a second digital revolution known as ‘Industry 4.00 [1]. Since it is essential to have adequate fault indices that could serve as indicators in order to predict early failures and create an effective PM schedule [2], in an Industry 4.0 context an appropriate approach would seem to be to distribute the tasks of CM/indices generation and PM analytics between the network edges and Core To this end, this paper is concerned with the development, validation and field-testing of a real-time CM/PM system for deployment upon large-scale pumping equipment in the water industry in an Industry 4.0 context. The principal goal of this paper is to help bridge the theory and practice gap by providing a detailed description and credible demonstration of an industrial implementation of a simple but effective edge device implementing this observer-based technique for fault detection of pumping equipment Both the observer itself, and the techniques applied to implement the observer in real-time on a small, low-cost portable embedded system, have a fundamental theoretical grounding.
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