Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities

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.

Automated summary

This paper provides a comprehensive survey of intelligent fault detection and diagnosis in district heating systems. It emphasizes the importance of maintaining an efficient heating system and discusses the use of artificial intelligence and machine learning techniques for automatic fault detection and diagnosis. The paper reviews 57 papers published in the last 12 years, highlights recent trends, identifies research gaps, discusses limitations, and provides recommendations for future studies.