Early fault detection method for nuclear power plants based on sparse denoising autoencoder and kernel principal component analysis
•A data-driven fault detection method is proposed, which can accurately detect early faults in nuclear power plants.•The proposed grouping strategy effectively overcomes the insensitivity of single models to early faults.•The combination of SDAE and KPCA demonstrates good feature extraction and nonl...
Gespeichert in:
| Veröffentlicht in: | Annals of nuclear energy Jg. 220; S. 111460 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Ltd
15.09.2025
|
| Schlagworte: | |
| ISSN: | 0306-4549 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | •A data-driven fault detection method is proposed, which can accurately detect early faults in nuclear power plants.•The proposed grouping strategy effectively overcomes the insensitivity of single models to early faults.•The combination of SDAE and KPCA demonstrates good feature extraction and nonlinear data processing capabilities.
Effective fault detection technology is of great significance to the safety and economy of nuclear power plants (NPPs). To accurately identify early faults in NPPs, this study proposes a novel fault detection method based on sparse denoising autoencoder (SDAE) and kernel principal component analysis (KPCA). First, the operating data of NPPs is collected by numerous sensors, and the operating parameters are grouped according to physical properties. Then, the corresponding fault detection model is established according to each parameter group, and each detection model consists of the SDAE and KPCA. The case study evaluated four accident scenarios (LOCA, SLBIC, FHAIC, FHAIAB) across two development degrees (0–1 % and 0–0.1 %). The proposed method achieved fault detection rates of 99.07 %, 95.20 %, 99.73 %, and 99.60 % for the 0–1 % degree with zero false alarms. Even for the subtler 0–0.1 % degree, it maintained a 94.84 % average detection rate and no false alarms. Compared to traditional methods, its average fault detection rate was higher than that of PCA and KPCA by 62.9 % and 32.4 % (0–1 % degree), and by 89.5 % and 88 % (0–0.1% degree), demonstrating its potential application value in NPPs. |
|---|---|
| AbstractList | •A data-driven fault detection method is proposed, which can accurately detect early faults in nuclear power plants.•The proposed grouping strategy effectively overcomes the insensitivity of single models to early faults.•The combination of SDAE and KPCA demonstrates good feature extraction and nonlinear data processing capabilities.
Effective fault detection technology is of great significance to the safety and economy of nuclear power plants (NPPs). To accurately identify early faults in NPPs, this study proposes a novel fault detection method based on sparse denoising autoencoder (SDAE) and kernel principal component analysis (KPCA). First, the operating data of NPPs is collected by numerous sensors, and the operating parameters are grouped according to physical properties. Then, the corresponding fault detection model is established according to each parameter group, and each detection model consists of the SDAE and KPCA. The case study evaluated four accident scenarios (LOCA, SLBIC, FHAIC, FHAIAB) across two development degrees (0–1 % and 0–0.1 %). The proposed method achieved fault detection rates of 99.07 %, 95.20 %, 99.73 %, and 99.60 % for the 0–1 % degree with zero false alarms. Even for the subtler 0–0.1 % degree, it maintained a 94.84 % average detection rate and no false alarms. Compared to traditional methods, its average fault detection rate was higher than that of PCA and KPCA by 62.9 % and 32.4 % (0–1 % degree), and by 89.5 % and 88 % (0–0.1% degree), demonstrating its potential application value in NPPs. |
| ArticleNumber | 111460 |
| Author | Ran, Wenhao Jia, Zhujun Yin, Wenzhe Xia, Hong Huang, Xueying Shan, Longfei |
| Author_xml | – sequence: 1 givenname: Wenzhe surname: Yin fullname: Yin, Wenzhe – sequence: 2 givenname: Hong surname: Xia fullname: Xia, Hong email: xiahong@hrbeu.edu.cn – sequence: 3 givenname: Xueying surname: Huang fullname: Huang, Xueying – sequence: 4 givenname: Longfei surname: Shan fullname: Shan, Longfei – sequence: 5 givenname: Wenhao surname: Ran fullname: Ran, Wenhao – sequence: 6 givenname: Zhujun surname: Jia fullname: Jia, Zhujun |
| BookMark | eNqFkM1OQyEQRlnUxLb6CCa8QCtwuTdtXBjT1J-kiRtdkykMSqVwA1TTtS8uTbty0w2zYM6X-c6IDEIMSMgNZ1POeHe7mULYaQw4FUy0U8657NiADFnDuols5fySjHLeMMbFTMoh-V1C8ntqYecLNVhQFxcD3WL5jIbamGiN8wiJ9vEH6-shlEzXkNHQuph7SBkrGaLLLnxQ2JWIQUdTlyEY-oUpoKd9ckG7HjzVcdvXm0Op3-D32eUrcmHBZ7w-zTF5f1y-LZ4nq9enl8XDaqIbzstEoEHOOhDAZmthbAvM2DnXKLWWnZWskd2cC2tn2IpZa5rWGoGNEVY23La8GZO7Y65OMeeEVmlX4NC3JHBecaYODtVGnRyqg0N1dFjp9h9dO20h7c9y90cOa7Vvh0ll7aohNC5V28pEdybhD24clvI |
| CitedBy_id | crossref_primary_10_1016_j_applthermaleng_2025_127831 crossref_primary_10_1016_j_compchemeng_2025_109394 |
| Cites_doi | 10.3390/s22062205 10.1002/aic.10978 10.1016/j.jpowsour.2006.07.004 10.1016/j.anucene.2024.110416 10.1016/j.jlp.2016.01.011 10.1016/j.ymssp.2020.106956 10.1080/00223131.2017.1394228 10.1109/TCST.2021.3107200 10.1093/nsr/nwx110 10.1016/j.pnucene.2021.103990 10.1038/323533a0 10.1016/j.jprocont.2020.11.005 10.1002/wics.101 10.13182/NT01-A3240 10.1038/s41597-022-01879-1 10.1016/j.anucene.2011.10.016 10.1016/j.measurement.2020.108388 10.1016/j.pnucene.2010.12.001 10.1016/j.anucene.2020.107786 10.1109/ACCESS.2018.2858277 10.1109/TAC.2003.822856 10.1109/TPWRD.2006.876659 10.1109/ACC.1995.529780 10.1016/j.jsv.2018.04.036 10.1016/j.compchemeng.2006.09.004 10.3390/en14164787 10.1016/j.pnucene.2018.06.003 10.1109/TR.2013.2285033 10.1162/089976698300017467 |
| ContentType | Journal Article |
| Copyright | 2025 Elsevier Ltd |
| Copyright_xml | – notice: 2025 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.anucene.2025.111460 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Physics |
| ExternalDocumentID | 10_1016_j_anucene_2025_111460 S0306454925002774 |
| GroupedDBID | --K --M -~X .~1 0R~ 1B1 1~. 1~5 23M 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JM 9JN AAEDT AAEDW AAHCO AAIKJ AAKOC AALRI AAOAW AAQFI AARJD AATTM AAXKI AAXUO AAYWO ABFYP ABJNI ABLST ABMAC ACDAQ ACGFS ACLOT ACRLP ADBBV ADEZE AEBSH AEIPS AEKER AENEX AEUPX AFJKZ AFPUW AFTJW AFXIZ AGHFR AGUBO AGYEJ AHEUO AHHHB AHIDL AIEXJ AIGII AIIUN AIKHN AITUG AKBMS AKIFW AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU APXCP AXJTR BELTK BKOJK BLECG BLXMC CS3 EBS EFJIC EFKBS EFLBG EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA IHE J1W JARJE KCYFY KOM LY6 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 ROL RPZ SDF SDG SDP SES SEW SPC SPCBC SPD SSJ SSR SSZ T5K ~G- ~HD .GJ 53G 6TJ 8WZ 9DU A6W AAQXK AAYXX ABFNM ABWVN ABXDB ACRPL ADMUD ADNMO AFFNX AGQPQ ASPBG AVWKF AZFZN CITATION EJD FEDTE FGOYB G-2 HVGLF HZ~ R2- SAC UHS WUQ |
| ID | FETCH-LOGICAL-c311t-2ede106a2a08b2df5a0df91ce4cc46f40346912ff8e5285d35fd2e3d2f431f513 |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001487154000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0306-4549 |
| IngestDate | Sat Nov 29 07:14:58 EST 2025 Tue Nov 18 21:02:26 EST 2025 Sat Nov 08 17:35:33 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Fault detection Nuclear power plant Kernel principal component analysis Early fault Sparse denoising autoencoder |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c311t-2ede106a2a08b2df5a0df91ce4cc46f40346912ff8e5285d35fd2e3d2f431f513 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_anucene_2025_111460 crossref_primary_10_1016_j_anucene_2025_111460 elsevier_sciencedirect_doi_10_1016_j_anucene_2025_111460 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-09-15 |
| PublicationDateYYYYMMDD | 2025-09-15 |
| PublicationDate_xml | – month: 09 year: 2025 text: 2025-09-15 day: 15 |
| PublicationDecade | 2020 |
| PublicationTitle | Annals of nuclear energy |
| PublicationYear | 2025 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Safaeipour, Forouzanfar, Casavola (b0100) 2021; 97 Simmini, Rampazzo, Peterle, Susto, Beghi (b0115) 2021; 30 Di Maio, Baraldi, Zio, Seraoui (b0020) 2013; 62 Elshenawy, Halawa, Mahmoud, Awad, Abdo (b0025) 2021; 142 Abdi, Williams (b0005) 2010; 2 Cheng, Shih, Chiang, Weng (b0015) 2012; 40 Li, Yang, Wang, Liu, Liang (b0055) 2018; 428 Wang, Liu (b0135) 2021; 168 Yan, Han (b0160) 2018; 6 Wang, Peng, Yu, Saeed, Hao, Liu (b0130) 2021; 150 Gertler, J. 1995. Diagnosing parametric faults: from parameter estimation to parity relations. In Proceedings of 1995 American Control Conference-ACC. 3, 1615-1620. Wen, Lu, Liu, Yan (b0145) 2020; 145 Rumelhart, Hinton, Williams (b0095) 1986; 323 Lee, Qin, Lee (b0045) 2006; 52 Qi, Xiao, Liang, Po, Zhang, Tong (b0090) 2022; 9 Peng, Xia, Liu, Yang, Guo, Zhu (b0085) 2018; 108 Kaistha, Upadhyaya (b0040) 2001; 136 Mansouri, Nounou, Nounou, Karim (b0075) 2016; 40 Schölkopf, Smola, Müller (b0105) 1998; 10 Liu, Abiodun, Zhi-bin, Mao-pu, Min-jun, Wei-feng (b0060) 2018; 55 Tamura, Tsujita (b0120) 2007; 31 Vincent, Larochelle, Bengio, Manzagol (b0125) 2008 Li (b0050) 2018; 5 Chen, Li, Tao, Barnett, Rudin, Su (b0010) 2019; 32 Guo, Zhang, Zhang, Zhou, Yu, Lei, Lv (b0035) 2021; 14 Mollah (b0080) 2018; 3 Silva, Souza, Brito (b0110) 2006; 21 Ye, Wang, Ding (b0165) 2004; 49 Mandal, Santhi, Sridhar, Vinolia, Swaminathan (b0070) 2017; 64 Xue, Tang, Sammes, Ding (b0155) 2006; 162 Webert, Döß, Kaupp, Simons (b0140) 2022; 22 Ma, Jiang (b0065) 2011; 53 Xu, Yao, Yong, Xia, Ge, Yu (b0150) 2024; 201 Wang (10.1016/j.anucene.2025.111460_b0135) 2021; 168 10.1016/j.anucene.2025.111460_b0030 Silva (10.1016/j.anucene.2025.111460_b0110) 2006; 21 Simmini (10.1016/j.anucene.2025.111460_b0115) 2021; 30 Liu (10.1016/j.anucene.2025.111460_b0060) 2018; 55 Xue (10.1016/j.anucene.2025.111460_b0155) 2006; 162 Wang (10.1016/j.anucene.2025.111460_b0130) 2021; 150 Peng (10.1016/j.anucene.2025.111460_b0085) 2018; 108 Xu (10.1016/j.anucene.2025.111460_b0150) 2024; 201 Lee (10.1016/j.anucene.2025.111460_b0045) 2006; 52 Li (10.1016/j.anucene.2025.111460_b0055) 2018; 428 Ye (10.1016/j.anucene.2025.111460_b0165) 2004; 49 Qi (10.1016/j.anucene.2025.111460_b0090) 2022; 9 Schölkopf (10.1016/j.anucene.2025.111460_b0105) 1998; 10 Wen (10.1016/j.anucene.2025.111460_b0145) 2020; 145 Abdi (10.1016/j.anucene.2025.111460_b0005) 2010; 2 Li (10.1016/j.anucene.2025.111460_b0050) 2018; 5 Tamura (10.1016/j.anucene.2025.111460_b0120) 2007; 31 Rumelhart (10.1016/j.anucene.2025.111460_b0095) 1986; 323 Mollah (10.1016/j.anucene.2025.111460_b0080) 2018; 3 Elshenawy (10.1016/j.anucene.2025.111460_b0025) 2021; 142 Yan (10.1016/j.anucene.2025.111460_b0160) 2018; 6 Safaeipour (10.1016/j.anucene.2025.111460_b0100) 2021; 97 Cheng (10.1016/j.anucene.2025.111460_b0015) 2012; 40 Webert (10.1016/j.anucene.2025.111460_b0140) 2022; 22 Chen (10.1016/j.anucene.2025.111460_b0010) 2019; 32 Di Maio (10.1016/j.anucene.2025.111460_b0020) 2013; 62 Mandal (10.1016/j.anucene.2025.111460_b0070) 2017; 64 Ma (10.1016/j.anucene.2025.111460_b0065) 2011; 53 Kaistha (10.1016/j.anucene.2025.111460_b0040) 2001; 136 Vincent (10.1016/j.anucene.2025.111460_b0125) 2008 Mansouri (10.1016/j.anucene.2025.111460_b0075) 2016; 40 Guo (10.1016/j.anucene.2025.111460_b0035) 2021; 14 |
| References_xml | – volume: 52 start-page: 3501 year: 2006 end-page: 3514 ident: b0045 article-title: Fault detection and diagnosis based on modified independent component analysis publication-title: AIChE J. – volume: 32 year: 2019 ident: b0010 article-title: This looks like that: deep learning for interpretable image recognition publication-title: Adv. Neural Inf. Proces. Syst. – volume: 145 year: 2020 ident: b0145 article-title: Graph modeling of singular values for early fault detection and diagnosis of rolling element bearings publication-title: Mech. Syst. Sig. Process. – volume: 31 start-page: 1035 year: 2007 end-page: 1046 ident: b0120 article-title: A study on the number of principal components and sensitivity of fault detection using PCA publication-title: Comput. Chem. Eng. – volume: 22 start-page: 2205 year: 2022 ident: b0140 article-title: Fault handling in industry 4.0: definition, process and applications publication-title: Sensors – volume: 162 start-page: 388 year: 2006 end-page: 399 ident: b0155 article-title: Model-based condition monitoring of PEM fuel cell using Hotelling T2 control limit publication-title: J. Power Sources – volume: 2 start-page: 433 year: 2010 end-page: 459 ident: b0005 article-title: Principal component analysis publication-title: Wiley Interdiscip. Rev. Comput. Stat. – volume: 53 start-page: 255 year: 2011 end-page: 266 ident: b0065 article-title: Applications of fault detection and diagnosis methods in nuclear power plants: a review publication-title: Prog. Nucl. Energy – volume: 55 start-page: 254 year: 2018 end-page: 266 ident: b0060 article-title: A cascade intelligent fault diagnostic technique for nuclear power plants publication-title: J. Nucl. Sci. Technol. – volume: 168 year: 2021 ident: b0135 article-title: Wind turbine condition monitoring based on a novel multivariate state estimation technique publication-title: Measurement – volume: 49 start-page: 281 year: 2004 end-page: 287 ident: b0165 article-title: A new parity space approach for fault detection based on stationary wavelet transform publication-title: IEEE Trans. Autom. Control – volume: 62 start-page: 833 year: 2013 end-page: 845 ident: b0020 article-title: Fault detection in nuclear power plants components by a combination of statistical methods publication-title: IEEE Trans. Reliab. – volume: 40 start-page: 334 year: 2016 end-page: 347 ident: b0075 article-title: Kernel PCA-based GLRT for nonlinear fault detection of chemical processes publication-title: J. Loss Prev. Process Ind. – volume: 10 start-page: 1299 year: 1998 end-page: 1319 ident: b0105 article-title: Nonlinear component analysis as a kernel eigenvalue problem publication-title: Neural Comput. – volume: 323 start-page: 533 year: 1986 end-page: 536 ident: b0095 article-title: Learning representations by back-propagating errors publication-title: Nature – volume: 6 start-page: 41238 year: 2018 end-page: 41248 ident: b0160 article-title: Effective feature extraction via stacked sparse autoencoder to improve intrusion detection system publication-title: IEEE Access – volume: 142 year: 2021 ident: b0025 article-title: Unsupervised machine learning techniques for fault detection and diagnosis in nuclear power plants publication-title: Prog. Nucl. Energy – volume: 9 start-page: 766 year: 2022 ident: b0090 article-title: An open time-series simulated dataset covering various accidents for nuclear power plants publication-title: Sci. Data – volume: 30 start-page: 1359 year: 2021 end-page: 1374 ident: b0115 article-title: A self-tuning KPCA-based approach to fault detection in chiller systems publication-title: IEEE Trans. Control Syst. Technol. – volume: 201 year: 2024 ident: b0150 article-title: Intelligent multi-severity nuclear accident identification under transferable operation conditions publication-title: Ann. Nucl. Energy – volume: 150 year: 2021 ident: b0130 article-title: Fault identification and diagnosis based on KPCA and similarity clustering for nuclear power plants publication-title: Ann. Nucl. Energy – start-page: 1096 year: 2008 end-page: 1103 ident: b0125 article-title: Extracting and composing robust features with denoising autoencoders publication-title: In Proceedings of the 25th International Conference on Machine Learning – volume: 136 start-page: 221 year: 2001 end-page: 230 ident: b0040 article-title: Incipient fault detection and isolation of field devices in nuclear power systems using principal component analysis publication-title: Nucl. Technol. – volume: 3 start-page: 1 year: 2018 end-page: 10 ident: b0080 article-title: PCTRAN: Education tool for simulation of safety and transient analysis of a pressurized water reactor publication-title: Int. J. Integr. Sci. Technol. – volume: 64 start-page: 1526 year: 2017 end-page: 1534 ident: b0070 article-title: Nuclear power plant thermocouple sensor-fault detection and classification using deep learning and generalized likelihood ratio test publication-title: IEEE Trans. Nucl. Sci. – volume: 40 start-page: 122 year: 2012 end-page: 129 ident: b0015 article-title: Introducing PCTRAN as an evaluation tool for nuclear power plant emergency responses publication-title: Ann. Nucl. Energy – volume: 21 start-page: 2058 year: 2006 end-page: 2063 ident: b0110 article-title: Fault detection and classification in transmission lines based on wavelet transform and ANN publication-title: IEEE Trans. Power Delivery – volume: 97 start-page: 1 year: 2021 end-page: 16 ident: b0100 article-title: A survey and classification of incipient fault diagnosis approaches publication-title: J. Process Control – volume: 5 start-page: 24 year: 2018 end-page: 26 ident: b0050 article-title: Deep learning for natural language processing: advantages and challenges publication-title: Natl. Sci. Rev. – volume: 108 start-page: 419 year: 2018 end-page: 427 ident: b0085 article-title: Research on intelligent fault diagnosis method for nuclear power plant based on correlation analysis and deep belief network publication-title: Prog. Nucl. Energy – volume: 428 start-page: 72 year: 2018 end-page: 86 ident: b0055 article-title: Early fault diagnosis of rolling bearings based on hierarchical symbol dynamic entropy and binary tree support vector machine publication-title: J. Sound Vib. – reference: Gertler, J. 1995. Diagnosing parametric faults: from parameter estimation to parity relations. In Proceedings of 1995 American Control Conference-ACC. 3, 1615-1620. – volume: 14 start-page: 4787 year: 2021 ident: b0035 article-title: An adaptive early fault detection model of induced draft fans based on multivariate state estimation technique publication-title: Energies – volume: 22 start-page: 2205 issue: 6 year: 2022 ident: 10.1016/j.anucene.2025.111460_b0140 article-title: Fault handling in industry 4.0: definition, process and applications publication-title: Sensors doi: 10.3390/s22062205 – volume: 52 start-page: 3501 issue: 10 year: 2006 ident: 10.1016/j.anucene.2025.111460_b0045 article-title: Fault detection and diagnosis based on modified independent component analysis publication-title: AIChE J. doi: 10.1002/aic.10978 – volume: 162 start-page: 388 issue: 1 year: 2006 ident: 10.1016/j.anucene.2025.111460_b0155 article-title: Model-based condition monitoring of PEM fuel cell using Hotelling T2 control limit publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2006.07.004 – volume: 201 year: 2024 ident: 10.1016/j.anucene.2025.111460_b0150 article-title: Intelligent multi-severity nuclear accident identification under transferable operation conditions publication-title: Ann. Nucl. Energy doi: 10.1016/j.anucene.2024.110416 – volume: 40 start-page: 334 year: 2016 ident: 10.1016/j.anucene.2025.111460_b0075 article-title: Kernel PCA-based GLRT for nonlinear fault detection of chemical processes publication-title: J. Loss Prev. Process Ind. doi: 10.1016/j.jlp.2016.01.011 – volume: 3 start-page: 1 year: 2018 ident: 10.1016/j.anucene.2025.111460_b0080 article-title: PCTRAN: Education tool for simulation of safety and transient analysis of a pressurized water reactor publication-title: Int. J. Integr. Sci. Technol. – volume: 145 year: 2020 ident: 10.1016/j.anucene.2025.111460_b0145 article-title: Graph modeling of singular values for early fault detection and diagnosis of rolling element bearings publication-title: Mech. Syst. Sig. Process. doi: 10.1016/j.ymssp.2020.106956 – volume: 55 start-page: 254 issue: 3 year: 2018 ident: 10.1016/j.anucene.2025.111460_b0060 article-title: A cascade intelligent fault diagnostic technique for nuclear power plants publication-title: J. Nucl. Sci. Technol. doi: 10.1080/00223131.2017.1394228 – volume: 30 start-page: 1359 issue: 4 year: 2021 ident: 10.1016/j.anucene.2025.111460_b0115 article-title: A self-tuning KPCA-based approach to fault detection in chiller systems publication-title: IEEE Trans. Control Syst. Technol. doi: 10.1109/TCST.2021.3107200 – volume: 5 start-page: 24 issue: 1 year: 2018 ident: 10.1016/j.anucene.2025.111460_b0050 article-title: Deep learning for natural language processing: advantages and challenges publication-title: Natl. Sci. Rev. doi: 10.1093/nsr/nwx110 – volume: 142 year: 2021 ident: 10.1016/j.anucene.2025.111460_b0025 article-title: Unsupervised machine learning techniques for fault detection and diagnosis in nuclear power plants publication-title: Prog. Nucl. Energy doi: 10.1016/j.pnucene.2021.103990 – volume: 32 year: 2019 ident: 10.1016/j.anucene.2025.111460_b0010 article-title: This looks like that: deep learning for interpretable image recognition publication-title: Adv. Neural Inf. Proces. Syst. – volume: 323 start-page: 533 issue: 6088 year: 1986 ident: 10.1016/j.anucene.2025.111460_b0095 article-title: Learning representations by back-propagating errors publication-title: Nature doi: 10.1038/323533a0 – volume: 97 start-page: 1 year: 2021 ident: 10.1016/j.anucene.2025.111460_b0100 article-title: A survey and classification of incipient fault diagnosis approaches publication-title: J. Process Control doi: 10.1016/j.jprocont.2020.11.005 – volume: 2 start-page: 433 issue: 4 year: 2010 ident: 10.1016/j.anucene.2025.111460_b0005 article-title: Principal component analysis publication-title: Wiley Interdiscip. Rev. Comput. Stat. doi: 10.1002/wics.101 – volume: 136 start-page: 221 issue: 2 year: 2001 ident: 10.1016/j.anucene.2025.111460_b0040 article-title: Incipient fault detection and isolation of field devices in nuclear power systems using principal component analysis publication-title: Nucl. Technol. doi: 10.13182/NT01-A3240 – volume: 9 start-page: 766 issue: 1 year: 2022 ident: 10.1016/j.anucene.2025.111460_b0090 article-title: An open time-series simulated dataset covering various accidents for nuclear power plants publication-title: Sci. Data doi: 10.1038/s41597-022-01879-1 – volume: 40 start-page: 122 issue: 1 year: 2012 ident: 10.1016/j.anucene.2025.111460_b0015 article-title: Introducing PCTRAN as an evaluation tool for nuclear power plant emergency responses publication-title: Ann. Nucl. Energy doi: 10.1016/j.anucene.2011.10.016 – volume: 168 year: 2021 ident: 10.1016/j.anucene.2025.111460_b0135 article-title: Wind turbine condition monitoring based on a novel multivariate state estimation technique publication-title: Measurement doi: 10.1016/j.measurement.2020.108388 – volume: 53 start-page: 255 issue: 3 year: 2011 ident: 10.1016/j.anucene.2025.111460_b0065 article-title: Applications of fault detection and diagnosis methods in nuclear power plants: a review publication-title: Prog. Nucl. Energy doi: 10.1016/j.pnucene.2010.12.001 – volume: 150 year: 2021 ident: 10.1016/j.anucene.2025.111460_b0130 article-title: Fault identification and diagnosis based on KPCA and similarity clustering for nuclear power plants publication-title: Ann. Nucl. Energy doi: 10.1016/j.anucene.2020.107786 – volume: 6 start-page: 41238 year: 2018 ident: 10.1016/j.anucene.2025.111460_b0160 article-title: Effective feature extraction via stacked sparse autoencoder to improve intrusion detection system publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2858277 – volume: 49 start-page: 281 issue: 2 year: 2004 ident: 10.1016/j.anucene.2025.111460_b0165 article-title: A new parity space approach for fault detection based on stationary wavelet transform publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.2003.822856 – volume: 21 start-page: 2058 issue: 4 year: 2006 ident: 10.1016/j.anucene.2025.111460_b0110 article-title: Fault detection and classification in transmission lines based on wavelet transform and ANN publication-title: IEEE Trans. Power Delivery doi: 10.1109/TPWRD.2006.876659 – ident: 10.1016/j.anucene.2025.111460_b0030 doi: 10.1109/ACC.1995.529780 – volume: 428 start-page: 72 year: 2018 ident: 10.1016/j.anucene.2025.111460_b0055 article-title: Early fault diagnosis of rolling bearings based on hierarchical symbol dynamic entropy and binary tree support vector machine publication-title: J. Sound Vib. doi: 10.1016/j.jsv.2018.04.036 – volume: 64 start-page: 1526 issue: 6 year: 2017 ident: 10.1016/j.anucene.2025.111460_b0070 article-title: Nuclear power plant thermocouple sensor-fault detection and classification using deep learning and generalized likelihood ratio test publication-title: IEEE Trans. Nucl. Sci. – volume: 31 start-page: 1035 issue: 9 year: 2007 ident: 10.1016/j.anucene.2025.111460_b0120 article-title: A study on the number of principal components and sensitivity of fault detection using PCA publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2006.09.004 – volume: 14 start-page: 4787 issue: 16 year: 2021 ident: 10.1016/j.anucene.2025.111460_b0035 article-title: An adaptive early fault detection model of induced draft fans based on multivariate state estimation technique publication-title: Energies doi: 10.3390/en14164787 – start-page: 1096 year: 2008 ident: 10.1016/j.anucene.2025.111460_b0125 article-title: Extracting and composing robust features with denoising autoencoders – volume: 108 start-page: 419 year: 2018 ident: 10.1016/j.anucene.2025.111460_b0085 article-title: Research on intelligent fault diagnosis method for nuclear power plant based on correlation analysis and deep belief network publication-title: Prog. Nucl. Energy doi: 10.1016/j.pnucene.2018.06.003 – volume: 62 start-page: 833 issue: 4 year: 2013 ident: 10.1016/j.anucene.2025.111460_b0020 article-title: Fault detection in nuclear power plants components by a combination of statistical methods publication-title: IEEE Trans. Reliab. doi: 10.1109/TR.2013.2285033 – volume: 10 start-page: 1299 issue: 5 year: 1998 ident: 10.1016/j.anucene.2025.111460_b0105 article-title: Nonlinear component analysis as a kernel eigenvalue problem publication-title: Neural Comput. doi: 10.1162/089976698300017467 |
| SSID | ssj0012844 |
| Score | 2.4071157 |
| Snippet | •A data-driven fault detection method is proposed, which can accurately detect early faults in nuclear power plants.•The proposed grouping strategy effectively... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 111460 |
| SubjectTerms | Early fault Fault detection Kernel principal component analysis Nuclear power plant Sparse denoising autoencoder |
| Title | Early fault detection method for nuclear power plants based on sparse denoising autoencoder and kernel principal component analysis |
| URI | https://dx.doi.org/10.1016/j.anucene.2025.111460 |
| Volume | 220 |
| WOSCitedRecordID | wos001487154000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 issn: 0306-4549 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0012844 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEF6FFiQ4ICggykt74FY5rNev9bFCRQWhCokicrOcfagpwYkSpypc-SP8VGZ31muDokIPXKxotQ8n82VmPJ75hpCXWpkkZ5JFWqRplMpcRaLOTZTnhY5td1uOhcLvi5MTMZmUH0ajn10tzMW8aBpxeVku_6uoYQyEbUtnryHusCkMwGcQOlxB7HD9J8EjZbGpN_P2QOlWYy9w7BTtkgobS2FsGa5tgzTbRtpmwlhrpuybA9Awq7UtpmoWMxdHqDftwrJdWtIJG2X_oleNnh8sMUrv6EW-LhcN5qojw8nQ4-0ZmrtztSs3DOoGSQw-6-b7WQDZBFN4jxferjrc-cj2ZKO_zfrxj2e-egLmGj0bRjF4ZlMusI6zq95iueVfL4eamXM20K2xLaBmW9U-RiDObbW2hC8xtieM-_m_02z_Yf5CUmKX73Ze-W0qu02F29wgu7zIStCbu4dvjybvwpsqMO9IUebvv68Se7X1frb7PwOf5vQeuesfRughgug-Gelmj9wZUFTukVsuRViuH5AfDljUAYsGYFEEFgVgUS9g6oBFEVjUAYvCRAQWDcCiA2BRABZFYNEALBqARTtgPSSf3hydvj6OfAuPSCZx3EZcKx2zvOY1E1OuTFYzZcpY6lTKNDcpS9K8jLkxQmdcZCrJjOI6UdyAY2uyOHlEdho46TGhRnChVM2FBLdyqlg5BdOiCi1YIg1P1T5Jux-2kp7f3rZZmVdXCnafjMOyJRK8_G2B6KRWeS8Vvc8K0Hj10ifXPespud3_WZ6RnXa10c_JTXnRztarFx6KvwBa6rzg |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Early+fault+detection+method+for+nuclear+power+plants+based+on+sparse+denoising+autoencoder+and+kernel+principal+component+analysis&rft.jtitle=Annals+of+nuclear+energy&rft.au=Yin%2C+Wenzhe&rft.au=Xia%2C+Hong&rft.au=Huang%2C+Xueying&rft.au=Shan%2C+Longfei&rft.date=2025-09-15&rft.issn=0306-4549&rft.volume=220&rft.spage=111460&rft_id=info:doi/10.1016%2Fj.anucene.2025.111460&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_anucene_2025_111460 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0306-4549&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0306-4549&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0306-4549&client=summon |