  The traditional PCA fault detection methods completely depend on the training
data. The prior knowledge such as the physical principle of the system has not
been taken into account. In this paper, we propose a new multi-PCA fault
detection model combined with prior knowledge. This new model can adapt to the
variable operating conditions of the central air conditioning system, and it
can detect small deviation faults of sensors and significantly shorten the time
delay of detecting drift faults. We also conducted enough ablation experiments
to demonstrate that our model is more robust and efficient.
