A fault diagnosis framework based on heterogeneous ensemble learning for air conditioning chiller with unbalanced samples

Abstract

Big data-based air conditioning fault diagnosis research has developed rapidly in recent years, but in actual engineering, the fault sample size of air conditioning systems is much smaller than the normal sample size, and the resulting sample imbalance problem makes conventional data-driven diagnostic methods based on low accuracy and poor stability. In order to solve the problem of unbalanced fault diagnosis of air-conditioning chillers, this paper proposes an integrated learning-based diagnostic model, which achieves diagnosis by combining multiple base models and by majority voting. The method uses four classification models, namely, random forest model, decision tree model, k nearest neighbor model, and isomorphic integration model, as base classifiers, and synthesizes the four base classifiers into a heterogeneous integration algorithmic model (IMV) through integrated learning, and performs diagnostic detection of seven types of typical faults of chiller units using the majority voting method of integrated learning. The effectiveness of the proposed algorithm is verified on the RP-1043 dataset, and the experimental results show that the accuracy of the heterogeneous integrated algorithm model (IMV) can reach 96.87%, which is a significant improvement compared with the accuracy of the other four base classifier models (81.04%–96.25%). Therefore, the integrated learning model has some application prospects in fault diagnosis when targeting unbalanced datasets.