Detection and identification of faults in a District Heating Network
District Heating Networks (DHNs) are composed of numerous pipes that can be threatened by faults that affect DHN operation and management. Thus, reliable diagnostic methodologies are essential to identify DHN health state and hinder DHN malfunctioning and performance deterioration. To this purpose,...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bahlawan, Hilal [verfasserIn] Ferraro, Niccolò [verfasserIn] Gambarotta, Agostino [verfasserIn] Losi, Enzo [verfasserIn] Manservigi, Lucrezia [verfasserIn] Morini, Mirko [verfasserIn] Saletti, Costanza [verfasserIn] Ruggero Spina, Pier [verfasserIn] Venturini, Mauro [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Energy conversion and management - Amsterdam [u.a.] : Elsevier Science, 1980, 266 |
|---|---|
| Übergeordnetes Werk: |
volume:266 |
| DOI / URN: |
10.1016/j.enconman.2022.115837 |
|---|
| Katalog-ID: |
ELV008090696 |
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| 520 | |a District Heating Networks (DHNs) are composed of numerous pipes that can be threatened by faults that affect DHN operation and management. Thus, reliable diagnostic methodologies are essential to identify DHN health state and hinder DHN malfunctioning and performance deterioration. To this purpose, a novel diagnostic approach that couples a DHN simulation model with an optimization algorithm for detecting and identifying both thermal and hydraulic faults, i.e., water leakages, anomalous heat and pressure losses, is presented in this paper. In the current paper, the novel diagnostic approach is challenged at evaluating the health state of the DHN of the campus of the University of Parma, where different faults are artificially implanted, by using a digital twin of the DHN. The faulty datasets account for both single and multiple faults, as well as different fault types and causes. | ||
| 650 | 4 | |a District heating networks | |
| 650 | 4 | |a Diagnostics | |
| 650 | 4 | |a Fault detection and identification | |
| 700 | 1 | |a Ferraro, Niccolò |e verfasserin |4 aut | |
| 700 | 1 | |a Gambarotta, Agostino |e verfasserin |4 aut | |
| 700 | 1 | |a Losi, Enzo |e verfasserin |4 aut | |
| 700 | 1 | |a Manservigi, Lucrezia |e verfasserin |4 aut | |
| 700 | 1 | |a Morini, Mirko |e verfasserin |4 aut | |
| 700 | 1 | |a Saletti, Costanza |e verfasserin |4 aut | |
| 700 | 1 | |a Ruggero Spina, Pier |e verfasserin |4 aut | |
| 700 | 1 | |a Venturini, Mauro |e verfasserin |4 aut | |
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10.1016/j.enconman.2022.115837 doi (DE-627)ELV008090696 (ELSEVIER)S0196-8904(22)00633-1 DE-627 ger DE-627 rda eng 620 DE-600 50.70 bkl 83.65 bkl 52.57 bkl 52.56 bkl Bahlawan, Hilal verfasserin aut Detection and identification of faults in a District Heating Network 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier District Heating Networks (DHNs) are composed of numerous pipes that can be threatened by faults that affect DHN operation and management. Thus, reliable diagnostic methodologies are essential to identify DHN health state and hinder DHN malfunctioning and performance deterioration. To this purpose, a novel diagnostic approach that couples a DHN simulation model with an optimization algorithm for detecting and identifying both thermal and hydraulic faults, i.e., water leakages, anomalous heat and pressure losses, is presented in this paper. In the current paper, the novel diagnostic approach is challenged at evaluating the health state of the DHN of the campus of the University of Parma, where different faults are artificially implanted, by using a digital twin of the DHN. The faulty datasets account for both single and multiple faults, as well as different fault types and causes. District heating networks Diagnostics Fault detection and identification Ferraro, Niccolò verfasserin aut Gambarotta, Agostino verfasserin aut Losi, Enzo verfasserin aut Manservigi, Lucrezia verfasserin aut Morini, Mirko verfasserin aut Saletti, Costanza verfasserin aut Ruggero Spina, Pier verfasserin aut Venturini, Mauro verfasserin aut Enthalten in Energy conversion and management Amsterdam [u.a.] : Elsevier Science, 1980 266 Online-Ressource (DE-627)320407659 (DE-600)2000891-0 (DE-576)12088352X nnns volume:266 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.70 Energie: Allgemeines 83.65 Versorgungswirtschaft 52.57 Energiespeicherung 52.56 Regenerative Energieformen alternative Energieformen AR 266 |
| spelling |
10.1016/j.enconman.2022.115837 doi (DE-627)ELV008090696 (ELSEVIER)S0196-8904(22)00633-1 DE-627 ger DE-627 rda eng 620 DE-600 50.70 bkl 83.65 bkl 52.57 bkl 52.56 bkl Bahlawan, Hilal verfasserin aut Detection and identification of faults in a District Heating Network 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier District Heating Networks (DHNs) are composed of numerous pipes that can be threatened by faults that affect DHN operation and management. Thus, reliable diagnostic methodologies are essential to identify DHN health state and hinder DHN malfunctioning and performance deterioration. To this purpose, a novel diagnostic approach that couples a DHN simulation model with an optimization algorithm for detecting and identifying both thermal and hydraulic faults, i.e., water leakages, anomalous heat and pressure losses, is presented in this paper. In the current paper, the novel diagnostic approach is challenged at evaluating the health state of the DHN of the campus of the University of Parma, where different faults are artificially implanted, by using a digital twin of the DHN. The faulty datasets account for both single and multiple faults, as well as different fault types and causes. District heating networks Diagnostics Fault detection and identification Ferraro, Niccolò verfasserin aut Gambarotta, Agostino verfasserin aut Losi, Enzo verfasserin aut Manservigi, Lucrezia verfasserin aut Morini, Mirko verfasserin aut Saletti, Costanza verfasserin aut Ruggero Spina, Pier verfasserin aut Venturini, Mauro verfasserin aut Enthalten in Energy conversion and management Amsterdam [u.a.] : Elsevier Science, 1980 266 Online-Ressource (DE-627)320407659 (DE-600)2000891-0 (DE-576)12088352X nnns volume:266 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.70 Energie: Allgemeines 83.65 Versorgungswirtschaft 52.57 Energiespeicherung 52.56 Regenerative Energieformen alternative Energieformen AR 266 |
| allfields_unstemmed |
10.1016/j.enconman.2022.115837 doi (DE-627)ELV008090696 (ELSEVIER)S0196-8904(22)00633-1 DE-627 ger DE-627 rda eng 620 DE-600 50.70 bkl 83.65 bkl 52.57 bkl 52.56 bkl Bahlawan, Hilal verfasserin aut Detection and identification of faults in a District Heating Network 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier District Heating Networks (DHNs) are composed of numerous pipes that can be threatened by faults that affect DHN operation and management. Thus, reliable diagnostic methodologies are essential to identify DHN health state and hinder DHN malfunctioning and performance deterioration. To this purpose, a novel diagnostic approach that couples a DHN simulation model with an optimization algorithm for detecting and identifying both thermal and hydraulic faults, i.e., water leakages, anomalous heat and pressure losses, is presented in this paper. In the current paper, the novel diagnostic approach is challenged at evaluating the health state of the DHN of the campus of the University of Parma, where different faults are artificially implanted, by using a digital twin of the DHN. The faulty datasets account for both single and multiple faults, as well as different fault types and causes. District heating networks Diagnostics Fault detection and identification Ferraro, Niccolò verfasserin aut Gambarotta, Agostino verfasserin aut Losi, Enzo verfasserin aut Manservigi, Lucrezia verfasserin aut Morini, Mirko verfasserin aut Saletti, Costanza verfasserin aut Ruggero Spina, Pier verfasserin aut Venturini, Mauro verfasserin aut Enthalten in Energy conversion and management Amsterdam [u.a.] : Elsevier Science, 1980 266 Online-Ressource (DE-627)320407659 (DE-600)2000891-0 (DE-576)12088352X nnns volume:266 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.70 Energie: Allgemeines 83.65 Versorgungswirtschaft 52.57 Energiespeicherung 52.56 Regenerative Energieformen alternative Energieformen AR 266 |
| allfieldsGer |
10.1016/j.enconman.2022.115837 doi (DE-627)ELV008090696 (ELSEVIER)S0196-8904(22)00633-1 DE-627 ger DE-627 rda eng 620 DE-600 50.70 bkl 83.65 bkl 52.57 bkl 52.56 bkl Bahlawan, Hilal verfasserin aut Detection and identification of faults in a District Heating Network 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier District Heating Networks (DHNs) are composed of numerous pipes that can be threatened by faults that affect DHN operation and management. Thus, reliable diagnostic methodologies are essential to identify DHN health state and hinder DHN malfunctioning and performance deterioration. To this purpose, a novel diagnostic approach that couples a DHN simulation model with an optimization algorithm for detecting and identifying both thermal and hydraulic faults, i.e., water leakages, anomalous heat and pressure losses, is presented in this paper. In the current paper, the novel diagnostic approach is challenged at evaluating the health state of the DHN of the campus of the University of Parma, where different faults are artificially implanted, by using a digital twin of the DHN. The faulty datasets account for both single and multiple faults, as well as different fault types and causes. District heating networks Diagnostics Fault detection and identification Ferraro, Niccolò verfasserin aut Gambarotta, Agostino verfasserin aut Losi, Enzo verfasserin aut Manservigi, Lucrezia verfasserin aut Morini, Mirko verfasserin aut Saletti, Costanza verfasserin aut Ruggero Spina, Pier verfasserin aut Venturini, Mauro verfasserin aut Enthalten in Energy conversion and management Amsterdam [u.a.] : Elsevier Science, 1980 266 Online-Ressource (DE-627)320407659 (DE-600)2000891-0 (DE-576)12088352X nnns volume:266 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.70 Energie: Allgemeines 83.65 Versorgungswirtschaft 52.57 Energiespeicherung 52.56 Regenerative Energieformen alternative Energieformen AR 266 |
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10.1016/j.enconman.2022.115837 doi (DE-627)ELV008090696 (ELSEVIER)S0196-8904(22)00633-1 DE-627 ger DE-627 rda eng 620 DE-600 50.70 bkl 83.65 bkl 52.57 bkl 52.56 bkl Bahlawan, Hilal verfasserin aut Detection and identification of faults in a District Heating Network 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier District Heating Networks (DHNs) are composed of numerous pipes that can be threatened by faults that affect DHN operation and management. Thus, reliable diagnostic methodologies are essential to identify DHN health state and hinder DHN malfunctioning and performance deterioration. To this purpose, a novel diagnostic approach that couples a DHN simulation model with an optimization algorithm for detecting and identifying both thermal and hydraulic faults, i.e., water leakages, anomalous heat and pressure losses, is presented in this paper. In the current paper, the novel diagnostic approach is challenged at evaluating the health state of the DHN of the campus of the University of Parma, where different faults are artificially implanted, by using a digital twin of the DHN. The faulty datasets account for both single and multiple faults, as well as different fault types and causes. District heating networks Diagnostics Fault detection and identification Ferraro, Niccolò verfasserin aut Gambarotta, Agostino verfasserin aut Losi, Enzo verfasserin aut Manservigi, Lucrezia verfasserin aut Morini, Mirko verfasserin aut Saletti, Costanza verfasserin aut Ruggero Spina, Pier verfasserin aut Venturini, Mauro verfasserin aut Enthalten in Energy conversion and management Amsterdam [u.a.] : Elsevier Science, 1980 266 Online-Ressource (DE-627)320407659 (DE-600)2000891-0 (DE-576)12088352X nnns volume:266 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.70 Energie: Allgemeines 83.65 Versorgungswirtschaft 52.57 Energiespeicherung 52.56 Regenerative Energieformen alternative Energieformen AR 266 |
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Enthalten in Energy conversion and management 266 volume:266 |
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Energie: Allgemeines Versorgungswirtschaft Energiespeicherung Regenerative Energieformen alternative Energieformen |
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District heating networks Diagnostics Fault detection and identification |
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Energy conversion and management |
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Bahlawan, Hilal @@aut@@ Ferraro, Niccolò @@aut@@ Gambarotta, Agostino @@aut@@ Losi, Enzo @@aut@@ Manservigi, Lucrezia @@aut@@ Morini, Mirko @@aut@@ Saletti, Costanza @@aut@@ Ruggero Spina, Pier @@aut@@ Venturini, Mauro @@aut@@ |
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2022-01-01T00:00:00Z |
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| author |
Bahlawan, Hilal |
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Bahlawan, Hilal ddc 620 bkl 50.70 bkl 83.65 bkl 52.57 bkl 52.56 misc District heating networks misc Diagnostics misc Fault detection and identification Detection and identification of faults in a District Heating Network |
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Detection and identification of faults in a District Heating Network |
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Bahlawan, Hilal Ferraro, Niccolò Gambarotta, Agostino Losi, Enzo Manservigi, Lucrezia Morini, Mirko Saletti, Costanza Ruggero Spina, Pier Venturini, Mauro |
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detection and identification of faults in a district heating network |
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Detection and identification of faults in a District Heating Network |
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District Heating Networks (DHNs) are composed of numerous pipes that can be threatened by faults that affect DHN operation and management. Thus, reliable diagnostic methodologies are essential to identify DHN health state and hinder DHN malfunctioning and performance deterioration. To this purpose, a novel diagnostic approach that couples a DHN simulation model with an optimization algorithm for detecting and identifying both thermal and hydraulic faults, i.e., water leakages, anomalous heat and pressure losses, is presented in this paper. In the current paper, the novel diagnostic approach is challenged at evaluating the health state of the DHN of the campus of the University of Parma, where different faults are artificially implanted, by using a digital twin of the DHN. The faulty datasets account for both single and multiple faults, as well as different fault types and causes. |
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District Heating Networks (DHNs) are composed of numerous pipes that can be threatened by faults that affect DHN operation and management. Thus, reliable diagnostic methodologies are essential to identify DHN health state and hinder DHN malfunctioning and performance deterioration. To this purpose, a novel diagnostic approach that couples a DHN simulation model with an optimization algorithm for detecting and identifying both thermal and hydraulic faults, i.e., water leakages, anomalous heat and pressure losses, is presented in this paper. In the current paper, the novel diagnostic approach is challenged at evaluating the health state of the DHN of the campus of the University of Parma, where different faults are artificially implanted, by using a digital twin of the DHN. The faulty datasets account for both single and multiple faults, as well as different fault types and causes. |
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District Heating Networks (DHNs) are composed of numerous pipes that can be threatened by faults that affect DHN operation and management. Thus, reliable diagnostic methodologies are essential to identify DHN health state and hinder DHN malfunctioning and performance deterioration. To this purpose, a novel diagnostic approach that couples a DHN simulation model with an optimization algorithm for detecting and identifying both thermal and hydraulic faults, i.e., water leakages, anomalous heat and pressure losses, is presented in this paper. In the current paper, the novel diagnostic approach is challenged at evaluating the health state of the DHN of the campus of the University of Parma, where different faults are artificially implanted, by using a digital twin of the DHN. The faulty datasets account for both single and multiple faults, as well as different fault types and causes. |
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Ferraro, Niccolò Gambarotta, Agostino Losi, Enzo Manservigi, Lucrezia Morini, Mirko Saletti, Costanza Ruggero Spina, Pier Venturini, Mauro |
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