Transfer learning methodology for machine learning based fault detection and diagnostics applied to building services

Abstract

Machine Learning (ML) models for Fault Detection and Diagnosis (FDD) can automatically detect anomalies in the operation in large facilities or district heating networks and can help tackling energy wastes. Nevertheless, the development of ML-models is a costly and tedious task requiring large amounts of labelled data. Setting up ML-models for a high number of systems is effort and know-how intensive. However, assets like commercial buildings and district heating networks are constituted of systems with similar topologies. Transferring a ML model initially trained on a source system to a multitude of similar target systems, can help reducing the training costs and facilitating the scalability of ML-based FDD in those assets. To enable this, we have developed a methodology that assesses the potential for Transfer Learning (TL) from a source system to target systems by determining the covariate and concept shifts between the source and target domains and integrating the source model into the target system if the TL assessment is positive. We used a patented method for the model development, that combines two ML-models, that are initially trained on a source system by means of a feedback system. We implemented this methodology on district heating (DH) substations, as DH systems typically contain this kind of subsystems with similar topologies and have thus a high scalability potential for TL. Initial findings showed the effectiveness of TL in adapting the source model to the target domain, resulting in enhanced FDD capabilities with significantly reduced training efforts.