A data-driven predictive maintenance model for hospital HVAC system with machine learning

Corrective and preventive maintenance strategies are typically employed to maintain an efficient functionality of different facility systems. This entails the evaluation of current conditions and the prediction of future conditions. Such prediction is highly needed for critical building systems such as Heating, Ventilation, and Air Conditioning (HVAC) of hospitals to maintain their functionality and extend their lifetime. Current literature highlights the benefits of adopting machine-learning algorithms for predictive modelling. Literature also reveals a gap in predictive modelling based on real-time sensor data and the prediction of both short-term and long-term future conditions. This paper presents a data-driven predictive maintenance model of a hospital's HVAC system with a focus on the Air Handling Units (AHUs). The developed model adopts machine-learning using the sensor data acquired by the BMS and the database of the hospital's CMMS. Support Vector Machine (SVM), Decision Trees (DT), and K-Nearest Neighbours (KNN) algorithms are used for the prediction of AHU's short-term conditions. Prophet Forecasting and Seasonal Auto-Regressive Integrated Moving Average (SARIMA) algorithms are then used to predict the AHU's long-term future conditions. The study also highlights the benefits of adopting the proposed model in terms of reduced maintenance cost and improved operational effectiveness of hospital AHUs.