A Multi-task Learning Governed Temporal Convolutional Network for Predicting Rare HVAC Compressor Faults

Prediction of the compressor faults in heating, ventilation and air conditioning (HVAC) based on collected data is critical to its operational safety and energy efficiency. Main challenges include the unclear mechanism of utilizing data to effectively predict the compressor faults and the impact of fault scarcity. To make a response, this study develops a novel multitask learning governed temporal convolutional network (MTTCN) for the rare HVAC compressor fault prediction task. The MTTCN adopts a TCN-based backbone guided by three designed learning tasks to derive valuable latent features from raw long-range inputs for more robust rare fault predictions. The primary rare fault prediction task is assisted by focal loss to overcome the fault scarcity, while two auxiliary tasks, the contrastive learning and condition prediction, are employed to sharpen the feature discrimination and augment the temporal prediction capabilities of MTTCN. To ensure an efficient and intelligent optimization process, an improved uncertainty-based dynamic weighting mechanism is developed to adaptively balance the weights of task-specific losses during training. Results of experiments conducted reveal the superior performance of MTTCN over existing models in predicting rare HVAC compressor faults.