A novel image-based transfer learning framework for cross-domain HVAC fault diagnosis: From multi-source data integration to knowledge sharing strategies

Data-driven classification models have gained increasing popularity for fault detection and diagnosis (FDD) tasks considering their advantages in implementation flexibility and modeling accuracies. To tackle the wide existence of data shortage challenges for individual buildings, transfer learning can be adopted to enhance the applicability of data-driven approaches. At present, limited studies have been conducted to explore the potentials of transfer learning in HVAC FDD tasks, leaving the following two key questions unanswered, i.e., (1) whether the tabular data collected from different building systems can be effectively integrated and utilized as the source data for transfer learning, and (2) whether the operational patterns learnt from a specific building system can be interchangeably applied for FDD tasks of other systems. This study proposes a novel image-based transfer learning framework to tackle the multi-source data compatibility challenge in the building field, while investigating the value of transfer learning in cross-domain FDD tasks. Data experiments have been designed to quantify the value of transfer learning given different data amounts, imbalance ratios, and transfer learning strategies. The research results validate the usefulness of image-based transfer learning for HVAC FDD tasks. The insights obtained are valuable for multi source building operational data integration and cross-domain knowledge sharing. (c) 2022 Elsevier B.V. All rights reserved.