A semi-supervised method for digital twin-enabled predictive maintenance in the building industry

The rapid booming of information and communication technologies (ICT) and artificial intelligence has promoted the development of maintenance practices in the building industry towards a data-driven paradigm, of which the digital twin (DT) serves as the fundamental framework to strengthen data transit ability and interoperability. Among the state-of-the-art approaches in the maintenance industry, predictive maintenance (PdM) is a prominent approach by anticipating unexpected failures and unscheduled maintenance tasks. However, most current PdM frameworks are facility-specific, lacking generality and scalability. Besides, existing solutions mainly concentrate on condition monitoring and fault identification rather than failure prediction. Moreover, good prediction results rely heavily on sufficient labelled data sets, which are costly and labour-intensive to collect. To address these issues, the author developed a unified PdM framework for the building industry from the DT perspective. Next, a novel failure prediction method utilising the Semi-supervised Generative Adversarial Network (GAN) has been proposed in this article, which makes effective utilisation of both labelled and unlabelled data. Finally, an online platform has been developed to present the monitoring and prediction information. Experimental findings show the effectiveness and superiority of the proposed method for failure prediction through public data sets of building facilities.