A distributed digital twin framework for effective asset management in industry 4.0

Abstract

Wind energy is crucial for achieving net-zero emissions by 2050. However, unexpected wind turbine failures, including fires and abrupt breakdowns, pose significant challenges to the reliability of wind energy. Predictive Maintenance (PdM) is an Asset Management (AM) strategy used to forecast equipment fault and resolve them before they occur, while Condition Monitoring (CM) is another asset management strategy used to monitor the condition of equipment at any given time. This work focuses on utilizing a technology called Digital Twin (DT) – a digital replica of wind turbines’ physical components in the digital domain, to support asset (equipment) management using predictive maintenance. This DT is used to achieve condition monitoring of wind turbine components and Machine Learning (ML) is applied to achieve predictive maintenance (PdM). The DT framework achieved in this work shows the use of streaming sensor data from the Supervisory Control and Data Acquisition (SCADA) system of wind turbines to constantly monitor the asset condition and use ML to anticipate component failures well in advance so as to provide operational teams with a cost-effective solution to simulate configurations that can help in managing turbine fault. While there have been successes in using digital twins and predictive maintenance in manufacturing systems, DT itself is an emerging technology that is still being explored as a concept. The explorations of the application of DT in literature highlighted various misconceptions, limitations and challenges that were necessary to look at in this study before achieving a wholistic framework that proposes a valuable and cost-effective DT implementation for the wind turbine predictive maintenance case study. To achieve the DT framework, multiple enabling technologies such as Industrial Internet of Things (IIoT), cloud computing and machine learning were considered and broken down into layers and applications. To standardize the DT framework, the ISO 23247 DT standard was used to guide the implementation of multiple DT architectures while evaluating various technology options. The methodology brought together and evaluated different machine learning techniques, software and hardware configurations, data streaming and storage technologies as well as architectural design patterns towards clarifying and validating the proposed DT framework. Experiments and simulations with open data, and results from real operational wind turbine SCADA data provided by an industry partner helped in validation. The PhD contribution is a functional architectural framework that is effective in the adoption of digital twins for predictive maintenance in wind turbines. Thus, the framework serves operational, resource utilization, scalability, standardization and cost benefits using a distributed digital twin approach for predictive maintenance. The work highlights and covers gaps from the associated challenges and limitations such as the lack of standardization found in earlier approaches in DT research within the domain of predictive maintenance in IIoT applications.