UAV image analysis for leakage detection in district heating systems using machine learning

Hossain, Kabir; Villebro, Frederik; Forchhammer, Søren

doi:10.1016/j.patrec.2020.05.024

Cited by 21 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although using image data directly eliminates the extra data conversion steps and potential information loss involved in this procedure, the model performance is influenced by the quality of the images depending on various factors such as the type of cameras, weather conditions, etc. [33].…”

Section: D Cnn Based Methodsmentioning

confidence: 99%

Deep learning in fault detection and diagnosis of building HVAC systems: A systematic review with meta analysis

Zhang

Saeed²,

Sadeghian³

2023

Energy and AI

View full text Add to dashboard Cite

Section: D Cnn Based Methodsmentioning

confidence: 99%

Deep learning in fault detection and diagnosis of building HVAC systems: A systematic review with meta analysis

Zhang

Saeed²,

Sadeghian³

2023

Energy and AI

View full text Add to dashboard Cite

“…Hossain et al [80] propose a leakage detection approach based on airborne thermal imagery using a CNN. The authors compare their approach to eight common ML algorithms.…”

Section: Leakage Detectionmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

“…In contrast, postprocessing of IR images may be computationally expensive, and their analysis could lead to false negatives because some color differences caused by a leakage could be almost inappreciable. Some authors such as Zhong et al (2019) [111] and Hossain et al (2020) [112] have developed ML algorithms to improve postprocessing and satisfactorily make the difference between true leakages and other potential causes.…”

Section: Leakage Detection In Dhc Networkmentioning

confidence: 99%

Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems—A Review

et al. 2021

View full text Add to dashboard Cite

Peak shaving, demand response, fast fault detection, emissions and costs reduction are some of the main objectives to meet in advanced district heating and cooling (DHC) systems. In order to enhance the operation of infrastructures, challenges such as supply temperature reduction and load uncertainty with the development of algorithms and technologies are growing. Therefore, traditional control strategies and diagnosis approaches cannot achieve these goals. Accordingly, to address these shortcomings, researchers have developed plenty of innovative methods based on their applications and features. The main purpose of this paper is to review recent publications that include both hard and soft computing implementations such as model predictive control and machine learning algorithms with applications also on both fourth and fifth generation district heating and cooling networks. After introducing traditional approaches, the innovative techniques, accomplished results and overview of the main strengths and weaknesses have been discussed together with a description of the main capabilities of some commercial platforms.

show abstract

UAV image analysis for leakage detection in district heating systems using machine learning

Cited by 21 publications

References 15 publications

Deep learning in fault detection and diagnosis of building HVAC systems: A systematic review with meta analysis

Deep learning in fault detection and diagnosis of building HVAC systems: A systematic review with meta analysis

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

Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems—A Review

Contact Info

Product

Resources

About