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

TLDR

The study implements and field‑tests an edge device for real‑time condition monitoring and fault detection of large‑scale rotating equipment in the UK water industry. The edge device runs a local digital twin on a microcontroller platform, using sliding‑mode observer soft sensors on low‑cost transducers, Modbus communication, and IP/GSM gateways, and was validated via hardware‑in‑the‑loop and field trials. The results indicate that the system offers a flexible, low‑cost digitalization platform for condition monitoring and predictive maintenance in the water sector.

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 weasr 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.