A Digital Twin-driven framework for predictive maintenance in water infrastructure using BIM and Deep Learning
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Asset maintenance management plays an integral role in the efficient operation of civil infrastructure. Predictive maintenance (PdM) is a technique, where multiple steps of data preparation and data analysis are employed to predict an asset fault. Despite the extensive application of PdM in manufacturing and oil industries, PdM implementation in critical civil infrastructure such as water infrastructure remains a challenge. This study proposes a digital twin-based asset maintenance management approach, which leverages deep learning, industry foundation classes, and building information modeling. Findings from this research are expected to facilitate effective PdM implementation in water infrastructure.
