This paper presents a comprehensive survey of state-of-the-art intelligent fault detection and diagnosis in district heating systems. Maintaining an efficient district heating system is crucial, as faults can lead to increased heat loss, customer discomfort, and operational cost. Intelligent fault detection and diagnosis can help to identify and diagnose faulty behavior automatically by utilizing artificial intelligence or machine learning. In our survey, we review and discuss 57 papers published in the last 12 years, highlight the recent trends, identify current research gaps, discuss the limitations of current techniques, and provide recommendations for future studies in this area. While there is an increasing interest in the topic, and the past five years have shown much advancement, the absence of open-source high-quality labeled data severely hinders progress. Future research should aim to explore transfer learning, domain adaptation, and semi-supervised learning to improve current performance. Additionally, a researcher should increase knowledge of district heating data using data-centric approaches to establish a solid foundation for future fault detection and diagnosis in district heating.

Fault detection in district heating (DH) substations is critical for energy efficiency and reliability. However, it is challenged by scarce fault labels, low‐frequency data, privacy concerns, and battery‐constrained gateways. We propose a novel hybrid semi‐supervised federated domain adaptation architecture for fault detection in DH. We use a one‐class variational autoencoder (VAE) to leverage heterogeneous sensor streams from 434 distributed substations. First, we perform cross‐network unsupervised pre‐training on multi‐sourced data from two independent real‐world DH networks, fusing their return temperature dynamics into a robust shared manifold. Second, we leverage maintenance metadata to selectively allow verified‐normal clients for per‐round fine‐tuning of the model. Third, we drastically reduce uplink costs by compressing each client's weight delta using 10% top‐ k sparsification and demonstrate that our pipeline enables robust few‐shot finetuning with 20% of the normal operational data while retaining high detection performance. By strategically training, our method achieves F ₁ and G‐mean scores of up to 97% and an AUC ≥ 99% on real‐world DH data. To our knowledge, this is the first work to study cross‐domain data fusion in the DH field for fault detection, aiming to enhance and enable effective, scalable, and energy‐efficient monitoring of substations.