Partial domain adaption based prediction calibration methodology for fault detection and diagnosis of chillers under variable operational condition scenarios

Data-driven fault detection and diagnosis (FDD) for building energy system has drawn much attention from both industry and academy due to their ability to learn complex relationships from big data. Most existing FDD studies only focus on the model diagnosis accuracy where the operational conditions of test dataset are similar with training dataset. However, it is rare for online operational data to be similar with offline training data. To better simulate the online environment in the aspect of operational conditions, a novel online data simulation method, which is named as variable operational conditions test, is proposed in this study. Furthermore, in order to improve the poor diagnosis performance of FDD models under variable operational condition scenarios, an unsupervised partial domain adaption algorithm is employed to calibrate the predictions of the FDD model within a short period of time. A comprehensive data experiment was conduct based on ASHARE RP-1043. The experimental results indicate the prediction calibration method yields decent performance improvement compared with three traditional data-driven-based FDD models, the highest and overall improvements are 55.07% and 14.5%, respectively. The research outcomes provide practical guidelines for the online application of FDD models when the training data cannot cover the whole sample space.

Automated summary

This study proposes an online data simulation method under variable operational conditions and improves the diagnostic performance of FDD models using an unsupervised partial domain adaption algorithm. Experimental results show that this method can achieve significant performance improvement.