Transfer learning-based strategies for fault diagnosis in building energy systems

Abstract Data-driven fault detection and diagnosis (FDD) in building energy systems is typically limited by the quantity and quality of training data. These methods can be only used for individual systems due to the insufficient extrapolation capabilities of most machine learning algorithms. A desirable solution is to utilize transfer learning, which can transfer the knowledge learned from data-rich building energy systems to FDD tasks in data-sparse systems. However, the potential of applying transfer learning to such FDD has not been systematically investigated. Accordingly, this paper proposes a transfer-learning-based methodology for fault diagnosis in building chillers. Experiments were conducted on two water-cooled screw chillers to collect both fault and fault-free data. Transfer-learning-based fault diagnosis experiments were implemented with consideration of different transfer learning tasks, training cases, learning scenarios, and transfer learning implementation strategies. The experimental results validate the value of transfer learning for FDD in building energy systems, especially when the experimental data available for model development are limited. The maximum accuracy improvements were 12.63% and 8.18% in the two learning tasks. The research outcomes provide practical guidelines for developing transfer-learning-based solutions for FDD in building energy systems.