Cross Domain Data Generation for Smart Building Fault Detection and Diagnosis

Abstract

Benefiting extensively from the Internet of Things (IoT) and sensor network technologies, the modern smart building achieves thermal comfort. It prevents energy wastage by performing automatic Fault Detection and Diagnosis (FDD) to maintain the good condition of its air-conditioning systems. Often, real-time multi-sensor measurements are collected, and supervised learning algorithms are adopted to exploit the data for an effective FDD. A key issue with the supervised methods is their dependence on well-labeled fault data, which is difficult to obtain in many real-world scenarios despite the abundance of unlabelled sensor data. Intuitively, the problem can be greatly alleviated if some well-labeled fault data collected under a particular setting can be re-used and transferred to other cases where labeled fault data is challenging or costly. Bearing this idea, we proposed a novel Adversarial Cross domain Data Generation (ACDG) framework to impute missing fault data for building fault detection and diagnosis where labeled data is costly. Unlike traditional Transfer Learning (TL)-related applications that adapt models or features learned in the source domain to the target domain, ACDG essentially “generates” the unknown sensor data for the target setting (target domain). This is accomplished by capturing the data patterns and common knowledge from known counterparts in the other setting (source domain), the inter-domain knowledge, and the intra-domain relations. The proposed ACDG framework is tested with the real-world Air Handling Unit (AHU) fault dataset of the ASHRAE Research Project 1312. Extensive experimental results on the cross-domain AHU fault data showed the effectiveness of ACDG in supplementing the data for a missing fault category by exploiting the underlying commonalities between different domain settings.