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 d...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/6/1448 |
| _version_ | 1797612202265411584 |
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| author | Jonne van Dreven Veselka Boeva Shahrooz Abghari Håkan Grahn Jad Al Koussa Emilia Motoasca |
| author_facet | Jonne van Dreven Veselka Boeva Shahrooz Abghari Håkan Grahn Jad Al Koussa Emilia Motoasca |
| author_sort | Jonne van Dreven |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T06:37:57Z |
| format | Article |
| id | doaj.art-91b4e7864d134bdbb11645012a6d5507 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T06:37:57Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-91b4e7864d134bdbb11645012a6d55072023-11-17T10:45:40ZengMDPI AGElectronics2079-92922023-03-01126144810.3390/electronics12061448Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and OpportunitiesJonne van Dreven0Veselka Boeva1Shahrooz Abghari2Håkan Grahn3Jad Al Koussa4Emilia Motoasca5Department of Computer Science, Blekinge Institute of Technology, 371 79 Karlskrona, SwedenDepartment of Computer Science, Blekinge Institute of Technology, 371 79 Karlskrona, SwedenDepartment of Computer Science, Blekinge Institute of Technology, 371 79 Karlskrona, SwedenDepartment of Computer Science, Blekinge Institute of Technology, 371 79 Karlskrona, SwedenUnit Energy Technology, Flemish Institute for Technological Research (VITO), Boeretang 200, 2400 Mol, BelgiumUnit Energy Technology, Flemish Institute for Technological Research (VITO), Boeretang 200, 2400 Mol, BelgiumThis 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.https://www.mdpi.com/2079-9292/12/6/1448artificial intelligencedata miningmachine learningreview |
| spellingShingle | Jonne van Dreven Veselka Boeva Shahrooz Abghari Håkan Grahn Jad Al Koussa Emilia Motoasca Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities Electronics artificial intelligence data mining machine learning review |
| title | Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities |
| title_full | Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities |
| title_fullStr | Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities |
| title_full_unstemmed | Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities |
| title_short | Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities |
| title_sort | intelligent approaches to fault detection and diagnosis in district heating current trends challenges and opportunities |
| topic | artificial intelligence data mining machine learning review |
| url | https://www.mdpi.com/2079-9292/12/6/1448 |
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