Multi-PCA based Fault Detection Model Combined with Prior knowledge of HVAC

TL;DR

This paper introduces a multi-PCA fault detection model for HVAC systems that incorporates prior knowledge, improving detection of small sensor deviations and reducing detection delay under variable conditions.

Contribution

The proposed model uniquely combines multi-PCA with prior system knowledge to enhance fault detection robustness and efficiency in HVAC systems.

Findings

More robust fault detection under variable conditions

Faster detection of sensor drift faults

Improved accuracy over traditional PCA methods

Abstract

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.