Digital twin (DT), aiming to characterise behaviors of physical entities by leveraging the virtual replica in real time, is an emerging technology and paradigm at the forefront of the Industry 4.0 revolution. The implementation of DT in predictive maintenance has facilitated its growth. As a major component of predictive maintenance, condition monitoring (CM) has great potential to combine with DT. To describe the state-of-the-art of DT-driven CM, this paper delivers a systematic review on the theoretical and practical development of DT in advancing CM. The evolution of concepts, main research areas, applied domains, and related key technologies are summarised. The driver of DT for CM is detailed in three aspects: data support, capability enhancement, and maintenance mode shift. The implementation process of DT-driven CM is introduced from the classification of DT modelling and the extension of monitoring algorithms. Finally, current challenges and opportunities for future research are discussed especially concerning the barriers and gaps in data management, high-fidelity modelling, behavior characterisation, framework standardisation, and uncertainty quantification.
Thermal barrier coating (TBC) has been used widely on turbine blades to provide temperature and oxidation protection. With the turbine inlet temperature continuously increasing, TBCs have become more likely to oxide spallation, leading to premature failure of blade metal substrates. Thus, It is necessary to accurately evaluate the in-service reliability of TBCs for blade life assessment and engine operation safety. Nowadays, it is common to dynamically record aero-engine operating and performance data, called dynamic covariate data, which provides periodic snapshots for obtaining reliability information of engine components. Nevertheless, existing TBC life prediction models that pay adequate attention to dynamic covariate information are rare. This paper focuses on using limited failure samples with associated dynamic covariate data to make in-service reliability assessments of TBCs through a proposed cumulative damage index model. For the demonstration of the proposed approach, an integrated TBC life simulation approach has been introduced, which comprises engine performance, blade thermal, TBC damage, and damage accumulation models. The case study shows that the proposed cumulative damage index model based method provides more stable and accurate results than the traditional statistical method based on failure-time data.
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