A comparison study on anomaly detection methods in manufacturing process monitoring with X-ray images

Process monitoring and anomaly detection in manufacturing production systems are of vital importance in the consistency, reliability, and quality of manufacturing operations, which motivates the development of a large number of anomaly detection methods. In this work, a comparison study on represent...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Journal of intelligent manufacturing Ročník 36; číslo 6; s. 4389 - 4409
Hlavní autoři:	Huang, Congfang, Blondheim, David, Zhou, Shiyu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.08.2025 Springer Nature B.V
Témata:	Anomalies Business and Management Control Criteria Decomposition Deep learning Hypothesis testing Intelligent manufacturing systems Machine learning Machines Manufacturing Manufacturing industry Mechatronics Monitoring Principal components analysis Processes Production Robotics Statistical analysis Statistical methods Statistics Tensor decomposition X-ray image Generative adversarial networks Anomaly detection Convolutional variational autoencoder Pattern extraction
ISSN:	0956-5515, 1572-8145
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Process monitoring and anomaly detection in manufacturing production systems are of vital importance in the consistency, reliability, and quality of manufacturing operations, which motivates the development of a large number of anomaly detection methods. In this work, a comparison study on representative unsupervised X-ray image based anomaly detection methods is conducted. Statistical, physical, and deep learning based dimension reduction methods and different anomaly detection criteria are considered and compared. Case studies on real-world X-ray image data with simulated anomalies and real anomalies are both carried out. Grey level co-occurrence matrix with Hotelling T 2 statistic as detection criteria achieves the best performance on the simulated anomalies case with an overall detection accuracy of 96%. Principal component analysis with reconstruction error as criteria obtains the highest detection rate of 90.6% in the real anomalies case. Considering the ever-growing availability of image data in intelligent manufacturing processes, this study will provide very useful knowledge and find a broad audience.
AbstractList	Process monitoring and anomaly detection in manufacturing production systems are of vital importance in the consistency, reliability, and quality of manufacturing operations, which motivates the development of a large number of anomaly detection methods. In this work, a comparison study on representative unsupervised X-ray image based anomaly detection methods is conducted. Statistical, physical, and deep learning based dimension reduction methods and different anomaly detection criteria are considered and compared. Case studies on real-world X-ray image data with simulated anomalies and real anomalies are both carried out. Grey level co-occurrence matrix with Hotelling T 2 statistic as detection criteria achieves the best performance on the simulated anomalies case with an overall detection accuracy of 96%. Principal component analysis with reconstruction error as criteria obtains the highest detection rate of 90.6% in the real anomalies case. Considering the ever-growing availability of image data in intelligent manufacturing processes, this study will provide very useful knowledge and find a broad audience. Process monitoring and anomaly detection in manufacturing production systems are of vital importance in the consistency, reliability, and quality of manufacturing operations, which motivates the development of a large number of anomaly detection methods. In this work, a comparison study on representative unsupervised X-ray image based anomaly detection methods is conducted. Statistical, physical, and deep learning based dimension reduction methods and different anomaly detection criteria are considered and compared. Case studies on real-world X-ray image data with simulated anomalies and real anomalies are both carried out. Grey level co-occurrence matrix with Hotelling T2 statistic as detection criteria achieves the best performance on the simulated anomalies case with an overall detection accuracy of 96%. Principal component analysis with reconstruction error as criteria obtains the highest detection rate of 90.6% in the real anomalies case. Considering the ever-growing availability of image data in intelligent manufacturing processes, this study will provide very useful knowledge and find a broad audience.
Author	Blondheim, David Zhou, Shiyu Huang, Congfang
Author_xml	– sequence: 1 givenname: Congfang orcidid: 0000-0003-0488-1573 surname: Huang fullname: Huang, Congfang organization: Department of Industrial and Systems Engineering, University of Wisconsin-Madison – sequence: 2 givenname: David orcidid: 0000-0002-2654-5821 surname: Blondheim fullname: Blondheim, David organization: Mercury Marine, a Division of Brunswick Corporation – sequence: 3 givenname: Shiyu orcidid: 0000-0002-5902-8812 surname: Zhou fullname: Zhou, Shiyu email: shiyuzhou@wisc.edu organization: Department of Industrial and Systems Engineering, University of Wisconsin-Madison
BookMark	eNp9kEtrwzAMgM3oYG23P7CTYedsfsRJfCxlLyjsssFuRkmc1qWJM9thzb-f2w4GO_QgLIQ-WfpmaNLZTiN0S8k9JSR_8JQUqUgISw_BRbK_QFMqcpYUNBUTNCVSZIkQVFyhmfdbQogsMjpFeoEr2_bgjLcd9mGoRxwT6GwLuxHXOugqmFhpddjY2mMTU-iGBqowONOtce9spb3Hre1MsMfStwkb_Jk4GLFpYa39NbpsYOf1ze87Rx9Pj-_Ll2T19vy6XKySilMZElFKwupMaIAMMilTDjKl0IgszYCIKucFcMmzUpZC5ETyBlhdFqzkpaxZWfA5ujvNjUt9DdoHtbWD6-KXijNOOGXRUuwqTl2Vs9473ajKBDhcGRyYnaJEHaSqk1QVhaqjVLWPKPuH9i6e6MbzED9Bvj_o0e5vqzPUD1WBjcQ
CitedBy_id	crossref_primary_10_1007_s10845_024_02511_2
Cites_doi	10.1109/TGRS.2017.2786718 10.1109/ITNEC48623.2020.9085163 10.1007/978-3-030-68799-1_35 10.1115/MSEC2023-105080 10.1109/TMI.2016.2535302 10.1016/j.compbiomed.2015.07.006 10.1002/aic.690370209 10.1109/IJCNN.2008.4634187 10.1109/CVPR.2019.00982 10.1109/JPROC.2021.3052449 10.1007/s10586-017-1117-8 10.1137/S0895479898346995 10.1109/ICTAI.2019.00028 10.1109/TNNLS.2020.3038659 10.1109/WACV57701.2024.00020 10.1109/CVPR.2016.70 10.1016/j.neucom.2013.09.055 10.1109/AISC56616.2023.10085348 10.1016/j.matpr.2020.03.622 10.1016/j.media.2019.01.010 10.1109/ICPR48806.2021.9412109 10.1007/BF02310791 10.3390/s20123336 10.1109/WACV51458.2022.00188 10.1007/978-3-030-61609-0_38 10.1007/978-3-319-46493-0_38 10.1016/j.knosys.2016.01.027 10.1007/978-3-030-33778-0_37 10.1016/j.neucom.2021.12.093 10.1145/3422622 10.1007/978-3-031-20056-4_23 10.1145/3097983.3098052 10.1016/j.ymssp.2019.106495 10.1007/BF02289464 10.1007/s10845-020-01583-0 10.1109/CVPR52688.2022.01392 10.1007/978-3-642-23783-6_41 10.1177/1475921710388972 10.1137/07070111X 10.1007/978-3-319-06605-9_6 10.1145/3439950 10.1080/00207549608905062 10.3390/rs15061679 10.1109/WACV57701.2024.00205 10.1109/LGRS.2015.2421813 10.21203/rs.3.rs-1211832/v1 10.1109/CVPR.2015.7298594 10.1109/COASE.2019.8843313 10.1007/s10845-021-01768-1 10.1109/ICASSP49357.2023.10096400 10.1007/s10762-010-9630-3 10.1002/smll.202205977 10.1504/IJCAET.2018.094328 10.1016/j.sigpro.2021.108370 10.1109/36.752194 10.1109/ACCESS.2019.2921912 10.1007/s10845-021-01842-8 10.1126/science.1127647 10.1109/JSTARS.2012.2189200 10.4249/scholarpedia.1883 10.1007/978-3-030-65414-6_11
ContentType	Journal Article
Copyright	The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Copyright_xml	– notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
DBID	AAYXX CITATION 7SC 7TB 8FD FR3 JQ2 K9. L7M L~C L~D
DOI	10.1007/s10845-024-02435-x
DatabaseName	CrossRef Computer and Information Systems Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database ProQuest Computer Science Collection ProQuest Health & Medical Complete (Alumni) Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional
DatabaseTitle	CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Health & Medical Complete (Alumni) Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional
DatabaseTitleList	Technology Research Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Computer Science Statistics
EISSN	1572-8145
EndPage	4409
ExternalDocumentID	10_1007_s10845_024_02435_x
GrantInformation_xml	– fundername: Division of Civil, Mechanical and Manufacturing Innovation grantid: 2323082 funderid: http://dx.doi.org/10.13039/100000147
GroupedDBID	-Y2 -~C -~X .4S .86 .DC .VR 06D 0R~ 0VY 1N0 1SB 2.D 203 28- 29K 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 3-Y 30V 4.4 406 408 409 40D 40E 5GY 5QI 5VS 67Z 6NX 78A 7WY 7X7 88E 8AO 8FE 8FG 8FJ 8FL 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAPKM AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYOK AAYQN AAYTO AAYZH ABAKF ABBBX ABBRH ABBXA ABDBE ABDZT ABECU ABFSG ABFTD ABFTV ABHLI ABHQN ABJCF ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABRTQ ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFO ACGFS ACHSB ACHXU ACIHN ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACREN ACSNA ACSTC ACZOJ ADHHG ADHIR ADHKG ADIMF ADKFA ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEAQA AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEUYN AEVLU AEXYK AEZWR AFBBN AFDZB AFEXP AFGCZ AFHIU AFKRA AFLOW AFOHR AFQWF AFWTZ AFYQB AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGQPQ AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHPBZ AHQJS AHSBF AHWEU AHYZX AI. AIAKS AIGIU AIIXL AILAN AITGF AIXLP AJBLW AJRNO AJZVZ AKVCP ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG ATHPR AVWKF AXYYD AYFIA AYQZM AZFZN AZQEC B-. BA0 BAPOH BBWZM BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ BSONS CAG CCPQU COF CS3 CSCUP D-I DDRTE DL5 DNIVK DPUIP DU5 DWQXO EBLON EBS EDO EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC FYUFA GGCAI GGRSB GJIRD GNUQQ GNWQR GQ7 GQ8 GROUPED_ABI_INFORM_RESEARCH GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K60 K6V K6~ K7- KDC KOV KOW L6V LAK LLZTM M0C M4Y M7S MA- MK~ ML~ N2Q NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I OAM OVD P19 P2P P62 P9P PF0 PHGZM PHGZT PQBIZ PQBZA PQGLB PQQKQ PROAC PT4 PT5 PTHSS Q2X QOK QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZC RZE RZK S16 S1Z S26 S27 S28 S3B SAP SBE SCF SCLPG SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TEORI TSG TSK TSV TUC TUS U2A U5U UG4 UOJIU UTJUX UZXMN VC2 VFIZW VH1 W23 W48 WK8 YLTOR Z45 ZMTXR ZYFGU ~A9 ~EX AAYXX AFFHD CITATION 7SC 7TB 8FD FR3 JQ2 K9. L7M L~C L~D
ID	FETCH-LOGICAL-c319t-5b902d65eaa6a69943a941af5646a05c738a3936b9b557093fa2db82b3b9d2b83
IEDL.DBID	RSV
ISICitedReferencesCount	1
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001271457600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0956-5515
IngestDate	Wed Nov 05 08:35:02 EST 2025 Sat Nov 29 07:34:47 EST 2025 Tue Nov 18 22:38:59 EST 2025 Wed Jul 16 06:29:01 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	6
Keywords	Tensor decomposition X-ray image Generative adversarial networks Anomaly detection Convolutional variational autoencoder Pattern extraction
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c319t-5b902d65eaa6a69943a941af5646a05c738a3936b9b557093fa2db82b3b9d2b83
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-0488-1573 0000-0002-2654-5821 0000-0002-5902-8812
PQID	3230312845
PQPubID	32407
PageCount	21
ParticipantIDs	proquest_journals_3230312845 crossref_citationtrail_10_1007_s10845_024_02435_x crossref_primary_10_1007_s10845_024_02435_x springer_journals_10_1007_s10845_024_02435_x
PublicationCentury	2000
PublicationDate	20250800 2025-08-00 20250801
PublicationDateYYYYMMDD	2025-08-01
PublicationDate_xml	– month: 8 year: 2025 text: 20250800
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York – name: London
PublicationTitle	Journal of intelligent manufacturing
PublicationTitleAbbrev	J Intell Manuf
PublicationYear	2025
Publisher	Springer US Springer Nature B.V
Publisher_xml	– name: Springer US – name: Springer Nature B.V
References	GE Hinton (2435_CR19) 2006; 313 S Larsen (2435_CR33) 2022; 33 2435_CR74 2435_CR73 2435_CR72 2435_CR78 2435_CR76 JD Carroll (2435_CR6) 1970; 35 T-W Tang (2435_CR65) 2020; 20 2435_CR79 JA Jablonski (2435_CR23) 2015; 12 N Tajbakhsh (2435_CR64) 2016; 35 2435_CR39 MA Kramer (2435_CR30) 1991; 37 R Kumari (2435_CR31) 2013; 3 M Fahim (2435_CR11) 2019; 7 LR Tucker (2435_CR67) 1966; 31 A Ng (2435_CR40) 2011; 72 L Huang (2435_CR21) 2006; 19 L Ruff (2435_CR52) 2021; 109 G Pang (2435_CR42) 2021; 54 2435_CR41 D Singh (2435_CR60) 2012; 1 L-K Soh (2435_CR62) 1999; 37 J An (2435_CR1) 2015; 2 SS Sarikan (2435_CR54) 2018; 140 2435_CR49 2435_CR48 A Karami (2435_CR26) 2012; 5 2435_CR47 I Goodfellow (2435_CR14) 2020; 63 L Ma (2435_CR36) 2010; 31 Y Pu (2435_CR46) 2016; 29 X Xia (2435_CR71) 2022; 493 P Zhan (2435_CR77) 2021; 32 2435_CR51 2435_CR50 I Goodfellow (2435_CR13) 2014; 27 2435_CR10 2435_CR53 2435_CR16 2435_CR15 2435_CR59 2435_CR58 2435_CR7 LE Peterson (2435_CR43) 2009; 4 2435_CR18 2435_CR8 2435_CR3 T Schlegl (2435_CR56) 2019; 54 P Vincent (2435_CR68) 2010; 11 2435_CR5 H Fanaee-T (2435_CR12) 2016; 98 E Prifti (2435_CR44) 2023; 19 W Shang (2435_CR57) 2023; 15 A Hage Chehade (2435_CR17) 2022; 45 TG Kolda (2435_CR29) 2009; 51 L Li (2435_CR34) 2020; 33 W Wang (2435_CR70) 2021; 32 L Mujica (2435_CR38) 2011; 10 SE Sofuoglu (2435_CR61) 2022; 192 2435_CR63 L De Lathauwer (2435_CR9) 2000; 21 2435_CR22 2435_CR66 2435_CR20 D Kwon (2435_CR32) 2019; 22 C-Y Liou (2435_CR35) 2014; 139 I Beheshti (2435_CR4) 2015; 64 2435_CR27 A Prokhorov (2435_CR45) 2001 2435_CR69 2435_CR24 2435_CR28 P Mohanaiah (2435_CR37) 2013; 3 H Sarmadi (2435_CR55) 2020; 140 F Aparisi (2435_CR2) 1996; 34 Q Zhang (2435_CR75) 2021; 34 J Kamalakannan (2435_CR25) 2018; 10
References_xml	– volume: 27 start-page: 2672 year: 2014 ident: 2435_CR13 publication-title: Advances in Neural Information Processing Systems – ident: 2435_CR72 doi: 10.1109/TGRS.2017.2786718 – ident: 2435_CR7 doi: 10.1109/ITNEC48623.2020.9085163 – ident: 2435_CR10 doi: 10.1007/978-3-030-68799-1_35 – ident: 2435_CR58 doi: 10.1115/MSEC2023-105080 – volume: 35 start-page: 1299 issue: 5 year: 2016 ident: 2435_CR64 publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/TMI.2016.2535302 – volume: 64 start-page: 208 year: 2015 ident: 2435_CR4 publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2015.07.006 – volume: 37 start-page: 233 issue: 2 year: 1991 ident: 2435_CR30 publication-title: AIChE Journal doi: 10.1002/aic.690370209 – ident: 2435_CR20 doi: 10.1109/IJCNN.2008.4634187 – ident: 2435_CR5 doi: 10.1109/CVPR.2019.00982 – volume: 109 start-page: 756 issue: 5 year: 2021 ident: 2435_CR52 publication-title: Proceedings of the IEEE doi: 10.1109/JPROC.2021.3052449 – volume: 19 start-page: 617 year: 2006 ident: 2435_CR21 publication-title: Advances in Neural Information Processing Systems – volume: 22 start-page: 949 year: 2019 ident: 2435_CR32 publication-title: Cluster Computing doi: 10.1007/s10586-017-1117-8 – volume: 21 start-page: 1324 issue: 4 year: 2000 ident: 2435_CR9 publication-title: SIAM Journal on Matrix Analysis and Applications doi: 10.1137/S0895479898346995 – ident: 2435_CR41 doi: 10.1109/ICTAI.2019.00028 – volume: 33 start-page: 1037 issue: 3 year: 2020 ident: 2435_CR34 publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2020.3038659 – ident: 2435_CR3 doi: 10.1109/WACV57701.2024.00020 – ident: 2435_CR76 doi: 10.1109/CVPR.2016.70 – volume: 139 start-page: 84 year: 2014 ident: 2435_CR35 publication-title: Neurocomputing doi: 10.1016/j.neucom.2013.09.055 – ident: 2435_CR59 – ident: 2435_CR74 – ident: 2435_CR39 doi: 10.1109/AISC56616.2023.10085348 – volume: 140 start-page: 64 year: 2018 ident: 2435_CR54 publication-title: Procedia Computer Science doi: 10.1016/j.matpr.2020.03.622 – volume: 54 start-page: 30 year: 2019 ident: 2435_CR56 publication-title: Medical Image Analysis doi: 10.1016/j.media.2019.01.010 – ident: 2435_CR50 doi: 10.1109/ICPR48806.2021.9412109 – volume: 35 start-page: 283 issue: 3 year: 1970 ident: 2435_CR6 publication-title: Psychometrika doi: 10.1007/BF02310791 – volume: 20 start-page: 3336 issue: 12 year: 2020 ident: 2435_CR65 publication-title: Sensors doi: 10.3390/s20123336 – ident: 2435_CR16 doi: 10.1109/WACV51458.2022.00188 – ident: 2435_CR47 doi: 10.1007/978-3-030-61609-0_38 – ident: 2435_CR15 – ident: 2435_CR18 doi: 10.1007/978-3-319-46493-0_38 – volume: 98 start-page: 130 year: 2016 ident: 2435_CR12 publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2016.01.027 – ident: 2435_CR53 doi: 10.1007/978-3-030-33778-0_37 – volume: 493 start-page: 497 year: 2022 ident: 2435_CR71 publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.12.093 – volume-title: Hotelling t2-distribution. Encyclopedia of Mathematics year: 2001 ident: 2435_CR45 – volume: 1 start-page: 243 issue: 6 year: 2012 ident: 2435_CR60 publication-title: International Journal of Engineering and Advanced Technology (IJEAT) – volume: 63 start-page: 139 issue: 11 year: 2020 ident: 2435_CR14 publication-title: Communications of the ACM doi: 10.1145/3422622 – ident: 2435_CR79 doi: 10.1007/978-3-031-20056-4_23 – ident: 2435_CR78 doi: 10.1145/3097983.3098052 – volume: 140 year: 2020 ident: 2435_CR55 publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2019.106495 – volume: 31 start-page: 279 issue: 3 year: 1966 ident: 2435_CR67 publication-title: Psychometrika doi: 10.1007/BF02289464 – volume: 32 start-page: 1711 year: 2021 ident: 2435_CR70 publication-title: Journal of Intelligent Manufacturing doi: 10.1007/s10845-020-01583-0 – volume: 11 start-page: 3371 issue: 12 year: 2010 ident: 2435_CR68 publication-title: Journal of Machine Learning Research – volume: 72 start-page: 1 issue: 2011 year: 2011 ident: 2435_CR40 publication-title: CS294A Lecture notes – ident: 2435_CR51 doi: 10.1109/CVPR52688.2022.01392 – ident: 2435_CR49 doi: 10.1007/978-3-642-23783-6_41 – volume: 10 start-page: 539 issue: 5 year: 2011 ident: 2435_CR38 publication-title: Structural Health Monitoring doi: 10.1177/1475921710388972 – volume: 51 start-page: 455 issue: 3 year: 2009 ident: 2435_CR29 publication-title: SIAM Review doi: 10.1137/07070111X – ident: 2435_CR66 – ident: 2435_CR24 doi: 10.1007/978-3-319-06605-9_6 – volume: 54 start-page: 1 issue: 2 year: 2021 ident: 2435_CR42 publication-title: ACM Computing Surveys (CSUR) doi: 10.1145/3439950 – volume: 34 start-page: 2853 issue: 10 year: 1996 ident: 2435_CR2 publication-title: International Journal of Production Research doi: 10.1080/00207549608905062 – volume: 15 start-page: 1679 issue: 6 year: 2023 ident: 2435_CR57 publication-title: Remote Sensing doi: 10.3390/rs15061679 – ident: 2435_CR22 doi: 10.1109/WACV57701.2024.00205 – volume: 12 start-page: 1725 issue: 8 year: 2015 ident: 2435_CR23 publication-title: IEEE Geoscience and Remote Sensing Letters doi: 10.1109/LGRS.2015.2421813 – volume: 34 start-page: 16280 year: 2021 ident: 2435_CR75 publication-title: Advances in Neural Information Processing Systems – volume: 2 start-page: 1 issue: 1 year: 2015 ident: 2435_CR1 publication-title: Special Lecture on IE – volume: 45 start-page: 729 issue: 3 year: 2022 ident: 2435_CR17 publication-title: Physical and Engineering Sciences in Medicine doi: 10.21203/rs.3.rs-1211832/v1 – ident: 2435_CR63 doi: 10.1109/CVPR.2015.7298594 – volume: 3 start-page: 1686 issue: 4 year: 2013 ident: 2435_CR31 publication-title: International Journal of Engineering Research and Applications – ident: 2435_CR8 – ident: 2435_CR73 doi: 10.1109/COASE.2019.8843313 – ident: 2435_CR69 – volume: 32 start-page: 1669 year: 2021 ident: 2435_CR77 publication-title: Journal of Intelligent Manufacturing doi: 10.1007/s10845-021-01768-1 – ident: 2435_CR28 doi: 10.1109/ICASSP49357.2023.10096400 – volume: 29 start-page: 2360 year: 2016 ident: 2435_CR46 publication-title: Advances in Neural Information Processing Systems – volume: 31 start-page: 753 issue: 6 year: 2010 ident: 2435_CR36 publication-title: Journal of Infrared, Millimeter, and Terahertz Waves doi: 10.1007/s10762-010-9630-3 – volume: 19 start-page: 2205977 issue: 16 year: 2023 ident: 2435_CR44 publication-title: Small (Weinheim an der Bergstrasse, Germany) doi: 10.1002/smll.202205977 – ident: 2435_CR48 – volume: 10 start-page: 504 issue: 5 year: 2018 ident: 2435_CR25 publication-title: International Journal of Computer Aided Engineering and Technology doi: 10.1504/IJCAET.2018.094328 – volume: 192 year: 2022 ident: 2435_CR61 publication-title: Signal Processing doi: 10.1016/j.sigpro.2021.108370 – volume: 37 start-page: 780 issue: 2 year: 1999 ident: 2435_CR62 publication-title: IEEE Transactions on Geoscience and Remote Sensing doi: 10.1109/36.752194 – volume: 7 start-page: 81664 year: 2019 ident: 2435_CR11 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2921912 – volume: 33 start-page: 457 issue: 2 year: 2022 ident: 2435_CR33 publication-title: Journal of Intelligent Manufacturing doi: 10.1007/s10845-021-01842-8 – volume: 313 start-page: 504 issue: 5786 year: 2006 ident: 2435_CR19 publication-title: Science doi: 10.1126/science.1127647 – volume: 5 start-page: 444 issue: 2 year: 2012 ident: 2435_CR26 publication-title: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing doi: 10.1109/JSTARS.2012.2189200 – volume: 4 start-page: 1883 issue: 2 year: 2009 ident: 2435_CR43 publication-title: Scholarpedia doi: 10.4249/scholarpedia.1883 – ident: 2435_CR27 doi: 10.1007/978-3-030-65414-6_11 – volume: 3 start-page: 1 issue: 5 year: 2013 ident: 2435_CR37 publication-title: International Journal of Scientific and Research Publications
SSID	ssj0009861
Score	2.4180627
Snippet	Process monitoring and anomaly detection in manufacturing production systems are of vital importance in the consistency, reliability, and quality of...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	4389
SubjectTerms	Anomalies Business and Management Control Criteria Decomposition Deep learning Hypothesis testing Intelligent manufacturing systems Machine learning Machines Manufacturing Manufacturing industry Mechatronics Monitoring Principal components analysis Processes Production Robotics Statistical analysis Statistical methods Statistics
Title	A comparison study on anomaly detection methods in manufacturing process monitoring with X-ray images
URI	https://link.springer.com/article/10.1007/s10845-024-02435-x https://www.proquest.com/docview/3230312845
Volume	36
WOSCitedRecordID	wos001271457600001&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: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1572-8145 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009861 issn: 0956-5515 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3PS8MwFA4yPejB6VScTsnBmwbapunyjkMcnob4i91KkqYw2LqxbuL-e5O0XaeooIdCaNNQ3kvy3mve-z6ErjR0Uy_QQBRVQEKufAJSpUTLRAkTjmmpPEc20R0M-HAID2VRWF5lu1dHkm6n3ih246GtJg7tRRkxnuO2MXfcEjY8Pr3WULvcoaQ6hD3jD7CyVOb7MT6bo9rH_HIs6qxNv_m_7zxA-6V3iXvFdDhEWzproWbF3IDLhdxCexswhEdI97Ba0xFiBziLTUNk04kYr3CiFy5fK8MF3XSOR6YpsqUtinBVjnhWlBvgidsh3C37gxcPyVys8GhiNq38GL30755v70lJv2D05sOCMAlekERMCxGJCCCkAkJfpCwKI-Ex1aVcUKCRBGlxvICmIkgkDySVkASS0xPUyKaZPrX5UyySqaB-ImQYCC4jDqkPJljSJkBjXhv5lRZiVWKTW4qMcVyjKlupxkaisZNq_N5G1-t3ZgUyx6-9O5Vy43KV5jE18Re1Bpq10U2lzPrxz6Od_a37OdoNLG2wyxvsoMZivtQXaEe9LUb5_NLN3g-fKesa
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1RS8MwEA4yBfXB6VScTs2Db1pom6RLHoc4Js4hOmVvJUlTGGzdWDdx_94kbe0UFfShENo0lLskd9fc9x0AF4o1Y9dXzJFIMgdT6TlMyNhRIpJch2NKSNcWm2j2enQwYA85KCwtst2LI0m7U6-A3Sg2aGJsLkQc7TmuY22xDGP-49NLSbVLLUuqZdjT_gDJoTLfj_HZHJU-5pdjUWtt2tX_fecu2Mm9S9jKpsMeWFNJDVSLyg0wX8g1sL1CQ7gPVAvKj3KE0BLOQt3gyWTMR0sYqbnN10pgVm46hUPd5MnCgCIsyhFOM7gBHNsdwt4yP3jhwJnxJRyO9aaVHoDn9k3_uuPk5Re03jw2d4hgrh8FRHEe8IAxjDjDHo9JgAPuEtlElCOGAsGE4fFiKOZ-JKgvkGCRLyg6BJVkkqgjkz9FAhFz5EVcYJ9TEVAWe0wHS0oHaMStA6_QQihzbnJTImMUlqzKRqqhlmhopRq-1cHlxzvTjJnj196NQrlhvkrTEOn4CxkDTergqlBm-fjn0Y7_1v0cbHb6992we9u7OwFbvikhbHMIG6Ayny3UKdiQr_NhOjuzM_kdrC_t_g
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1dS8MwFA0yRfTB6VScTs2Db1rWNk2WPA51KMoY-MHeSpKmMNi6sXbi_r1J2m5TVBAfCqFNQ8nNx73NPecAcKFYK3Z9xRyJJHMCKj2HCRk7SkSS63BMCelasYlWt0v7fdZbQfHbbPfySDLHNBiWpiRrTqK4uQJ8o4FBFgfmQtjRXuR6YBLpTbz-9Lqk3aWWMdWy7WnfABewme_b-Lw1Lf3NL0ekdufpVP__zbtgp_A6YTsfJntgTSU1UC0VHWAxwWtge4WecB-oNpQLmUJoiWihLvBkPOLDOYxUZvO4EpjLUKdwoIs8mRmwhEU_wkkOQ4Aju3LYW-bHL-w7Uz6Hg5FezNID8NK5fb6-cwpZBm1Pj2UOFsz1I4IV54QTxgLEWeDxGJOAcBfLFqIcMUQEE4bfi6GY-5GgvkCCRb6g6BBUknGijkxeFSYi5siLuAh8TgWhLPaYDqKUDtywWwdeaZFQFpzlRjpjGC7Zlk2vhrpHQ9ur4XsdXC7emeSMHb_WbpSGDovZm4ZIx2XIbNy4Dq5Kwy4f_9za8d-qn4PN3k0nfLzvPpyALd8oC9vUwgaoZNOZOgUb8i0bpNMzO6g_AGp89uI
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=A+comparison+study+on+anomaly+detection+methods+in+manufacturing+process+monitoring+with+X-ray+images&rft.jtitle=Journal+of+intelligent+manufacturing&rft.au=Huang%2C+Congfang&rft.au=Blondheim%2C+David&rft.au=Zhou%2C+Shiyu&rft.date=2025-08-01&rft.issn=0956-5515&rft.eissn=1572-8145&rft.volume=36&rft.issue=6&rft.spage=4389&rft.epage=4409&rft_id=info:doi/10.1007%2Fs10845-024-02435-x&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s10845_024_02435_x
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0956-5515&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0956-5515&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0956-5515&client=summon