Towards a Distributed Digital Twin Framework for Predictive Maintenance in Industrial Internet of Things (IIoT)

Abstract

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

Immersive Digital Twin under ISO 23247 Applied to Flexible Manufacturing Processes

“…Within the state of the art, it is observed that most of the works presented by other authors do not follow a standard for the design of DT architectures; however, as a developing technology, several industries and universities have focused their attention on it. For the design of 3D environments, several CAD version 2022 softwares are used: among the main ones are Blender and Solid Work; for communication, the most used protocols are OPC UA, TCP/IP, AMQP, MODBUS, and MQTT; and in the physical environment, the process data are obtained by PLCs or embedded systems [20,29,31,32]. In addition, the integration of several technologies is evidenced to enhance the characteristics of the DT.…”

“…One of the areas where DTs are used is in maintenance, where several investigations have been carried out. In [20][21][22], they present a DT to perform the predictive maintenance of a wind turbine and reliability in floating offshore wind turbines (FOWTs). The architecture presents a distributed DT with IIoT, machine learning (ML), MQTT, and fog computing to offer the better utilization and management of assets through real-time condition monitoring, predictive analysis, and the health management of selected wind turbine components in a wind farm.…”