Wind turbine fault detection based on deep residual networks
Condition monitoring and fault detection for wind turbines (WTs) can effectively lower the effect of failures. A large amount of data would be generated during the operation of WTs, and these data have the following characteristics: (1) The data volumes are too large, and it is difficult to extract...
Ausführliche Beschreibung
Autor*in: |
Liu, Jiayang [verfasserIn] Wang, Xiaosun [verfasserIn] Wu, Shijing [verfasserIn] Wan, Liang [verfasserIn] Xie, Fuqi [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: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 213 |
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Übergeordnetes Werk: |
volume:213 |
DOI / URN: |
10.1016/j.eswa.2022.119102 |
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Katalog-ID: |
ELV008799164 |
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520 | |a Condition monitoring and fault detection for wind turbines (WTs) can effectively lower the effect of failures. A large amount of data would be generated during the operation of WTs, and these data have the following characteristics: (1) The data volumes are too large, and it is difficult to extract fault features. (2) Signals are correlated, and a fault may lead to multiple sensors to alarm. (3) Dimension and orders of magnitude are different between parameters. (4) The supervisory control and data acquisition (SCADA) data are fluctuating, which can easily lead to false alarms and missed alarms with certain one-sidedness. (5) Fault alarms possess a lag. To solve the problems of inaccurate and untimely fault detection (FD) caused by these data characteristics, a new deep network called deep residual network (DRN) is proposed in this paper for WTs’ detection. In the proposed method, the raw data collected by SCADA system are directly applied as the inputs of the DRN. Then, a convolutional residual building block (CRBB) is established by using convolutional layers, squeeze and excitation units. Meanwhile, the improved meta-ACON (active or not) is introduced to replace of rectified linear unit (ReLU). The high-level features are extracted from the raw data by stacking multiple CRBBs. Finally, the FD results are obtained by feeding the extracted features to the softmax classifier. The proposed DRN is validated by using the data from the SCADA system. The results indicate that the proposed DRN achieves better performance, and outperforms some published fault detection methods. | ||
650 | 4 | |a Wind turbine | |
650 | 4 | |a Fault detection | |
650 | 4 | |a Deep residual network | |
650 | 4 | |a Squeeze and excitation operations | |
700 | 1 | |a Wang, Xiaosun |e verfasserin |4 aut | |
700 | 1 | |a Wu, Shijing |e verfasserin |4 aut | |
700 | 1 | |a Wan, Liang |e verfasserin |4 aut | |
700 | 1 | |a Xie, Fuqi |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Expert systems with applications |d Amsterdam [u.a.] : Elsevier Science, 1990 |g 213 |h Online-Ressource |w (DE-627)320577961 |w (DE-600)2017237-0 |w (DE-576)11481807X |7 nnns |
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2022 |
allfields |
10.1016/j.eswa.2022.119102 doi (DE-627)ELV008799164 (ELSEVIER)S0957-4174(22)02120-0 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Liu, Jiayang verfasserin aut Wind turbine fault detection based on deep residual networks 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Condition monitoring and fault detection for wind turbines (WTs) can effectively lower the effect of failures. A large amount of data would be generated during the operation of WTs, and these data have the following characteristics: (1) The data volumes are too large, and it is difficult to extract fault features. (2) Signals are correlated, and a fault may lead to multiple sensors to alarm. (3) Dimension and orders of magnitude are different between parameters. (4) The supervisory control and data acquisition (SCADA) data are fluctuating, which can easily lead to false alarms and missed alarms with certain one-sidedness. (5) Fault alarms possess a lag. To solve the problems of inaccurate and untimely fault detection (FD) caused by these data characteristics, a new deep network called deep residual network (DRN) is proposed in this paper for WTs’ detection. In the proposed method, the raw data collected by SCADA system are directly applied as the inputs of the DRN. Then, a convolutional residual building block (CRBB) is established by using convolutional layers, squeeze and excitation units. Meanwhile, the improved meta-ACON (active or not) is introduced to replace of rectified linear unit (ReLU). The high-level features are extracted from the raw data by stacking multiple CRBBs. Finally, the FD results are obtained by feeding the extracted features to the softmax classifier. The proposed DRN is validated by using the data from the SCADA system. The results indicate that the proposed DRN achieves better performance, and outperforms some published fault detection methods. Wind turbine Fault detection Deep residual network Squeeze and excitation operations Wang, Xiaosun verfasserin aut Wu, Shijing verfasserin aut Wan, Liang verfasserin aut Xie, Fuqi verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 213 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:213 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_101 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_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_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_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_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 54.72 Künstliche Intelligenz VZ AR 213 |
spelling |
10.1016/j.eswa.2022.119102 doi (DE-627)ELV008799164 (ELSEVIER)S0957-4174(22)02120-0 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Liu, Jiayang verfasserin aut Wind turbine fault detection based on deep residual networks 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Condition monitoring and fault detection for wind turbines (WTs) can effectively lower the effect of failures. A large amount of data would be generated during the operation of WTs, and these data have the following characteristics: (1) The data volumes are too large, and it is difficult to extract fault features. (2) Signals are correlated, and a fault may lead to multiple sensors to alarm. (3) Dimension and orders of magnitude are different between parameters. (4) The supervisory control and data acquisition (SCADA) data are fluctuating, which can easily lead to false alarms and missed alarms with certain one-sidedness. (5) Fault alarms possess a lag. To solve the problems of inaccurate and untimely fault detection (FD) caused by these data characteristics, a new deep network called deep residual network (DRN) is proposed in this paper for WTs’ detection. In the proposed method, the raw data collected by SCADA system are directly applied as the inputs of the DRN. Then, a convolutional residual building block (CRBB) is established by using convolutional layers, squeeze and excitation units. Meanwhile, the improved meta-ACON (active or not) is introduced to replace of rectified linear unit (ReLU). The high-level features are extracted from the raw data by stacking multiple CRBBs. Finally, the FD results are obtained by feeding the extracted features to the softmax classifier. The proposed DRN is validated by using the data from the SCADA system. The results indicate that the proposed DRN achieves better performance, and outperforms some published fault detection methods. Wind turbine Fault detection Deep residual network Squeeze and excitation operations Wang, Xiaosun verfasserin aut Wu, Shijing verfasserin aut Wan, Liang verfasserin aut Xie, Fuqi verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 213 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:213 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_101 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_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_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_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_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 54.72 Künstliche Intelligenz VZ AR 213 |
allfields_unstemmed |
10.1016/j.eswa.2022.119102 doi (DE-627)ELV008799164 (ELSEVIER)S0957-4174(22)02120-0 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Liu, Jiayang verfasserin aut Wind turbine fault detection based on deep residual networks 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Condition monitoring and fault detection for wind turbines (WTs) can effectively lower the effect of failures. A large amount of data would be generated during the operation of WTs, and these data have the following characteristics: (1) The data volumes are too large, and it is difficult to extract fault features. (2) Signals are correlated, and a fault may lead to multiple sensors to alarm. (3) Dimension and orders of magnitude are different between parameters. (4) The supervisory control and data acquisition (SCADA) data are fluctuating, which can easily lead to false alarms and missed alarms with certain one-sidedness. (5) Fault alarms possess a lag. To solve the problems of inaccurate and untimely fault detection (FD) caused by these data characteristics, a new deep network called deep residual network (DRN) is proposed in this paper for WTs’ detection. In the proposed method, the raw data collected by SCADA system are directly applied as the inputs of the DRN. Then, a convolutional residual building block (CRBB) is established by using convolutional layers, squeeze and excitation units. Meanwhile, the improved meta-ACON (active or not) is introduced to replace of rectified linear unit (ReLU). The high-level features are extracted from the raw data by stacking multiple CRBBs. Finally, the FD results are obtained by feeding the extracted features to the softmax classifier. The proposed DRN is validated by using the data from the SCADA system. The results indicate that the proposed DRN achieves better performance, and outperforms some published fault detection methods. Wind turbine Fault detection Deep residual network Squeeze and excitation operations Wang, Xiaosun verfasserin aut Wu, Shijing verfasserin aut Wan, Liang verfasserin aut Xie, Fuqi verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 213 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:213 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_101 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_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_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_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_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 54.72 Künstliche Intelligenz VZ AR 213 |
allfieldsGer |
10.1016/j.eswa.2022.119102 doi (DE-627)ELV008799164 (ELSEVIER)S0957-4174(22)02120-0 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Liu, Jiayang verfasserin aut Wind turbine fault detection based on deep residual networks 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Condition monitoring and fault detection for wind turbines (WTs) can effectively lower the effect of failures. A large amount of data would be generated during the operation of WTs, and these data have the following characteristics: (1) The data volumes are too large, and it is difficult to extract fault features. (2) Signals are correlated, and a fault may lead to multiple sensors to alarm. (3) Dimension and orders of magnitude are different between parameters. (4) The supervisory control and data acquisition (SCADA) data are fluctuating, which can easily lead to false alarms and missed alarms with certain one-sidedness. (5) Fault alarms possess a lag. To solve the problems of inaccurate and untimely fault detection (FD) caused by these data characteristics, a new deep network called deep residual network (DRN) is proposed in this paper for WTs’ detection. In the proposed method, the raw data collected by SCADA system are directly applied as the inputs of the DRN. Then, a convolutional residual building block (CRBB) is established by using convolutional layers, squeeze and excitation units. Meanwhile, the improved meta-ACON (active or not) is introduced to replace of rectified linear unit (ReLU). The high-level features are extracted from the raw data by stacking multiple CRBBs. Finally, the FD results are obtained by feeding the extracted features to the softmax classifier. The proposed DRN is validated by using the data from the SCADA system. The results indicate that the proposed DRN achieves better performance, and outperforms some published fault detection methods. Wind turbine Fault detection Deep residual network Squeeze and excitation operations Wang, Xiaosun verfasserin aut Wu, Shijing verfasserin aut Wan, Liang verfasserin aut Xie, Fuqi verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 213 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:213 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_101 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_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_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_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_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 54.72 Künstliche Intelligenz VZ AR 213 |
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Expert systems with applications |
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Expert systems with applications |
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eng |
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000 - Computer science, information & general works |
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2022 |
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author_browse |
Liu, Jiayang Wang, Xiaosun Wu, Shijing Wan, Liang Xie, Fuqi |
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format_se |
Elektronische Aufsätze |
author-letter |
Liu, Jiayang |
doi_str_mv |
10.1016/j.eswa.2022.119102 |
dewey-full |
004 |
author2-role |
verfasserin |
title_sort |
wind turbine fault detection based on deep residual networks |
title_auth |
Wind turbine fault detection based on deep residual networks |
abstract |
Condition monitoring and fault detection for wind turbines (WTs) can effectively lower the effect of failures. A large amount of data would be generated during the operation of WTs, and these data have the following characteristics: (1) The data volumes are too large, and it is difficult to extract fault features. (2) Signals are correlated, and a fault may lead to multiple sensors to alarm. (3) Dimension and orders of magnitude are different between parameters. (4) The supervisory control and data acquisition (SCADA) data are fluctuating, which can easily lead to false alarms and missed alarms with certain one-sidedness. (5) Fault alarms possess a lag. To solve the problems of inaccurate and untimely fault detection (FD) caused by these data characteristics, a new deep network called deep residual network (DRN) is proposed in this paper for WTs’ detection. In the proposed method, the raw data collected by SCADA system are directly applied as the inputs of the DRN. Then, a convolutional residual building block (CRBB) is established by using convolutional layers, squeeze and excitation units. Meanwhile, the improved meta-ACON (active or not) is introduced to replace of rectified linear unit (ReLU). The high-level features are extracted from the raw data by stacking multiple CRBBs. Finally, the FD results are obtained by feeding the extracted features to the softmax classifier. The proposed DRN is validated by using the data from the SCADA system. The results indicate that the proposed DRN achieves better performance, and outperforms some published fault detection methods. |
abstractGer |
Condition monitoring and fault detection for wind turbines (WTs) can effectively lower the effect of failures. A large amount of data would be generated during the operation of WTs, and these data have the following characteristics: (1) The data volumes are too large, and it is difficult to extract fault features. (2) Signals are correlated, and a fault may lead to multiple sensors to alarm. (3) Dimension and orders of magnitude are different between parameters. (4) The supervisory control and data acquisition (SCADA) data are fluctuating, which can easily lead to false alarms and missed alarms with certain one-sidedness. (5) Fault alarms possess a lag. To solve the problems of inaccurate and untimely fault detection (FD) caused by these data characteristics, a new deep network called deep residual network (DRN) is proposed in this paper for WTs’ detection. In the proposed method, the raw data collected by SCADA system are directly applied as the inputs of the DRN. Then, a convolutional residual building block (CRBB) is established by using convolutional layers, squeeze and excitation units. Meanwhile, the improved meta-ACON (active or not) is introduced to replace of rectified linear unit (ReLU). The high-level features are extracted from the raw data by stacking multiple CRBBs. Finally, the FD results are obtained by feeding the extracted features to the softmax classifier. The proposed DRN is validated by using the data from the SCADA system. The results indicate that the proposed DRN achieves better performance, and outperforms some published fault detection methods. |
abstract_unstemmed |
Condition monitoring and fault detection for wind turbines (WTs) can effectively lower the effect of failures. A large amount of data would be generated during the operation of WTs, and these data have the following characteristics: (1) The data volumes are too large, and it is difficult to extract fault features. (2) Signals are correlated, and a fault may lead to multiple sensors to alarm. (3) Dimension and orders of magnitude are different between parameters. (4) The supervisory control and data acquisition (SCADA) data are fluctuating, which can easily lead to false alarms and missed alarms with certain one-sidedness. (5) Fault alarms possess a lag. To solve the problems of inaccurate and untimely fault detection (FD) caused by these data characteristics, a new deep network called deep residual network (DRN) is proposed in this paper for WTs’ detection. In the proposed method, the raw data collected by SCADA system are directly applied as the inputs of the DRN. Then, a convolutional residual building block (CRBB) is established by using convolutional layers, squeeze and excitation units. Meanwhile, the improved meta-ACON (active or not) is introduced to replace of rectified linear unit (ReLU). The high-level features are extracted from the raw data by stacking multiple CRBBs. Finally, the FD results are obtained by feeding the extracted features to the softmax classifier. The proposed DRN is validated by using the data from the SCADA system. The results indicate that the proposed DRN achieves better performance, and outperforms some published fault detection methods. |
collection_details |
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title_short |
Wind turbine fault detection based on deep residual networks |
remote_bool |
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author2 |
Wang, Xiaosun Wu, Shijing Wan, Liang Xie, Fuqi |
author2Str |
Wang, Xiaosun Wu, Shijing Wan, Liang Xie, Fuqi |
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doi_str |
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up_date |
2024-07-06T20:56:33.817Z |
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