Digital twin-enhanced predictive maintenance for indoor climate: A parallel LSTM-autoencoder failure prediction approach

Abstract

Recently, the emergency of predictive maintenance (PdM) in the building industry has expanded from facilities to indoor climates, as air quality is highly relevant to residential health, comfort, and work efficiency. Besides, digital twin (DT) is considered as an effective solution for PdM deployment because it significantly enhances data-driven insights and enables proactive interventions. However, most existing studies on indoor climate focus on condition monitoring or anomaly detection rather than failure prediction, which has higher requirements for data and algorithms. In this study, the remaining useful life (RUL) and time shift (TS) methods are employed to split the prediction problem into the combination of a supervised and an unsupervised subtask, followed by the development of a parallel prediction model integrating the long short-term memory network (LSTM) and autoencoder (AE) methods. Besides, a DT-enabled PdM framework has been proposed for indoor climates, validated through the establishment of an online platform designed to reconstruct the 3D building model and demonstrate real-time monitoring and alert information of indoor climates. Experiments show the effectiveness of the proposed model under different warning times and fault severity through a comparison study with other 4 benchmark models based on a practical dataset collected from different buildings in Singapore, while the practical online platform serves as an illustrative case for future DT-enhanced PdM solutions.