The CM and fault detection is achieved with an edge device implementing sliding-mode observers as soft sensors, which are designed to monitor equipment locally in a water pumping station. 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. In addition, 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, 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. As with other Internet-of-Things and Industry 4.0 applications, CM/PM applications can potentially generate large volumes of high-velocity real-time data therefore, it is beneficial to run local processing of this data at the edge of the network (on an ‘edge device’), to reduce the frequency and real-time requirements of communication to core devices. At the heart of this aspect of industrial digitalization the topics of real-time Condition Monitoring (CM) and Predictive Maintenance (PM) can be found. The 100% availability notion captures the vision that in the future there will be only ever be planned maintenance stops while clearly never achievable in practice, the right balance between maintenance cost and risk must clearly be aimed for. It has previously been suggested that one of the so-called grand challenges which can be addressed by digitization is the notion of the ‘sustainable, 100% available plant’. 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. 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 4.0′. 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. 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 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. 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 edge device implements a local digital twin, processing information from low-cost transducers mounted on the equipment in real-time. 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.
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