Multiclass data classification using fault detection-based techniques
Multiclass classification of big data is a subject of broad interest in machine learning research nowadays, where it is necessary to extract important features from a dataset’s variables in order to accurately detect unique variations between different classes of data. In this paper, we will discuss...
Saved in:
| Published in | Computers & chemical engineering Vol. 136; p. 106786 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
08.05.2020
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0098-1354 1873-4375 |
| DOI | 10.1016/j.compchemeng.2020.106786 |
Cover
| Abstract | Multiclass classification of big data is a subject of broad interest in machine learning research nowadays, where it is necessary to extract important features from a dataset’s variables in order to accurately detect unique variations between different classes of data. In this paper, we will discuss the application of a novel combination of different fault detection-based techniques towards the problem of multiclass classification of different types of faults found in the benchmark Tennessee Eastman Process. Moreover, the fault detection performance and data classification accuracy of our proposed method is compared to the respective performances of multiple data-driven methods tabulated in literature, including deep neural networks. The results show that a combined application of multiple fault detection techniques, in tandem with the one-versus-all and all-versus-all binary decomposition methods, can provide a competitive multiclass classification accuracy, comparative to other more complex methods in literature. |
|---|---|
| AbstractList | Multiclass classification of big data is a subject of broad interest in machine learning research nowadays, where it is necessary to extract important features from a dataset’s variables in order to accurately detect unique variations between different classes of data. In this paper, we will discuss the application of a novel combination of different fault detection-based techniques towards the problem of multiclass classification of different types of faults found in the benchmark Tennessee Eastman Process. Moreover, the fault detection performance and data classification accuracy of our proposed method is compared to the respective performances of multiple data-driven methods tabulated in literature, including deep neural networks. The results show that a combined application of multiple fault detection techniques, in tandem with the one-versus-all and all-versus-all binary decomposition methods, can provide a competitive multiclass classification accuracy, comparative to other more complex methods in literature. |
| ArticleNumber | 106786 |
| Author | Nounou, Mohamed Nounou, Hazem Basha, Nour Kravaris, Costas Ziyan Sheriff, M. |
| Author_xml | – sequence: 1 givenname: Nour surname: Basha fullname: Basha, Nour organization: Chemical Engineering Department, Texas A&M University at Qatar, Education City, 23874, Doha, Qatar – sequence: 2 givenname: M. surname: Ziyan Sheriff fullname: Ziyan Sheriff, M. organization: Chemical Engineering Department, Texas A&M University at Qatar, Education City, 23874, Doha, Qatar – sequence: 3 givenname: Costas surname: Kravaris fullname: Kravaris, Costas organization: Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA – sequence: 4 givenname: Hazem surname: Nounou fullname: Nounou, Hazem organization: Electrical and Computer Engineering Department, Texas A&M University at Qatar, Education City, 23874, Doha, Qatar – sequence: 5 givenname: Mohamed surname: Nounou fullname: Nounou, Mohamed email: mohamed.nounou@qatar.tamu.edu organization: Chemical Engineering Department, Texas A&M University at Qatar, Education City, 23874, Doha, Qatar |
| BookMark | eNqNkMtOwzAQRS1UJNrCP4QPSPErtrNCqCoPqYgNrC3XnrSuUqfYLhJ_T9KyQKy6mpmruVczZ4JGoQuA0C3BM4KJuNvObLfb2w3sIKxnFNNBF1KJCzQmSrKSM1mN0BjjWpWEVfwKTVLaYowpV2qMFq-HNnvbmpQKZ7Ipjq1vvDXZd6E4JB_WRWP6rcJBBjuo5cokcEU_bYL_PEC6RpeNaRPc_NYp-nhcvM-fy-Xb08v8YVlaRkkuBSgsQHCJCXBc10ANNjVlxhrTNL2qqKJiZaWrhOPCEmBcWlVRWREJdcWm6P6Ua2OXUoRGW5-Ph-ZofKsJ1gMVvdV_qOiBij5R6RPqfwn76Hcmfp_lnZ-80L_45SHqZD0EC87HHox2nT8j5QdZhYc1 |
| CitedBy_id | crossref_primary_10_1007_s00521_024_10551_1 crossref_primary_10_1016_j_jngse_2020_103460 crossref_primary_10_1109_TII_2023_3242811 crossref_primary_10_1016_j_jgsce_2023_204964 crossref_primary_10_1016_j_jprocont_2021_10_001 crossref_primary_10_1016_j_compchemeng_2025_109098 crossref_primary_10_1049_ise2_12115 crossref_primary_10_1016_j_cherd_2022_05_022 crossref_primary_10_1016_j_jii_2021_100216 crossref_primary_10_3389_fcomp_2023_1211739 crossref_primary_10_3390_math8081263 crossref_primary_10_3389_fenrg_2024_1351665 crossref_primary_10_1016_j_compchemeng_2022_108126 crossref_primary_10_1177_01423312221099855 crossref_primary_10_1016_j_compchemeng_2022_107733 crossref_primary_10_1109_OJIM_2022_3232650 crossref_primary_10_1109_TII_2022_3166784 crossref_primary_10_1016_j_neucom_2021_11_067 crossref_primary_10_1016_j_psep_2023_06_041 crossref_primary_10_1016_j_epsr_2023_109532 crossref_primary_10_1016_j_jpse_2022_100089 crossref_primary_10_1016_j_measurement_2022_111181 |
| Cites_doi | 10.1109/TCBB.2012.113 10.1016/j.ces.2008.10.012 10.3390/pr7070411 10.1007/3-540-27373-5_21 10.1080/00224065.2010.11917825 10.1198/004017005000000256 10.1016/j.jocs.2018.04.017 10.1016/j.asoc.2010.04.012 10.1016/0169-7439(95)00076-3 10.3182/20110828-6-IT-1002.02876 10.1016/0098-1354(93)80018-I 10.1214/aos/1028144844 10.1023/A:1022627411411 10.1080/00224065.2013.11917913 10.1023/A:1009715923555 10.1016/j.compchemeng.2018.04.009 10.1016/j.jocs.2018.05.013 10.1016/j.ifacol.2018.09.380 10.1016/j.jprocont.2012.06.009 10.2307/2530946 10.1007/s001800050038 10.1016/j.compchemeng.2017.02.041 10.1080/00224065.2013.11917914 |
| ContentType | Journal Article |
| Copyright | 2020 |
| Copyright_xml | – notice: 2020 |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.compchemeng.2020.106786 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1873-4375 |
| ExternalDocumentID | 10_1016_j_compchemeng_2020_106786 S0098135420300090 |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAIAV AAIKC AAIKJ AAKOC AALRI AAMNW AAOAW AAQFI AAXUO ABJNI ABMAC ABNUV ABYKQ ACDAQ ACGFS ACRLP ADBBV ADEWK ADEZE ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHPOS AIEXJ AIKHN AITUG AJOXV AKURH ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AXJTR BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG ENUVR EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA IHE J1W JJJVA KOM LG9 LX7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 ROL RPZ SBC SDF SDG SDP SES SPC SPCBC SSG SST SSZ T5K ~G- 29F AAQXK AATTM AAXKI AAYWO AAYXX ABFNM ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADMUD ADNMO AEIPS AEUPX AFFNX AFJKZ AFPUW AGQPQ AI. AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN BBWZM CITATION EFKBS EJD FEDTE FGOYB HLY HLZ HVGLF HZ~ NDZJH R2- SCE SEW VH1 WUQ ZY4 ~HD |
| ID | FETCH-LOGICAL-c321t-6e806e64701e4099e2a0a923acaaff70182826bc7d56d46c1e347c8527517e953 |
| IEDL.DBID | .~1 |
| ISSN | 0098-1354 |
| IngestDate | Thu Apr 24 22:51:36 EDT 2025 Thu Oct 02 04:32:27 EDT 2025 Fri Feb 23 02:48:28 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Hypothesis testing Moving-window Fault detection Binary decomposition Generalized likelihood ratio test Interval aggregation Multiclass classification Principal component analysis |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c321t-6e806e64701e4099e2a0a923acaaff70182826bc7d56d46c1e347c8527517e953 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_compchemeng_2020_106786 crossref_primary_10_1016_j_compchemeng_2020_106786 elsevier_sciencedirect_doi_10_1016_j_compchemeng_2020_106786 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-05-08 |
| PublicationDateYYYYMMDD | 2020-05-08 |
| PublicationDate_xml | – month: 05 year: 2020 text: 2020-05-08 day: 08 |
| PublicationDecade | 2020 |
| PublicationTitle | Computers & chemical engineering |
| PublicationYear | 2020 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Allwein, Schapire, Singer (bib0001) 2001; 1 Qiu (bib0036) 2013 Tharrault, Mourot, Ragot, Harkat (bib0050) 2010 Zhang, Zhao (bib0058) 2017; 107 Lauro, Palumbo (bib0027) 2005 Cortes, Vapnik (bib0012) 1995; 20 Wang, Reynolds (bib0052) 2013; 45 Lauro, Verde, Irpino (bib0028) 2008 Burges (bib0010) 1998; 2 Heo, Lee (bib0020) 2019; 7 Rish (bib0043) 2001; 3 Downs, Vogel (bib0014) 1993; 17 Hekmati, MohammadM., Abbasi Nozari, Aliyari, Simani (bib0018) 2010 Hyvärinen, Karhunen, Oja (bib0022) 2001; 26 Breiman, Friedman, Olshen, Stone (bib0009) 1984; 40 Rifkin, Klautau (bib0042) 2004; 5 Basha (bib0003) 2018 Hastie, Tibshirani (bib0017) 1998; 26 Woodall, Mahmoud (bib0053) 2005; 47 Izem, Bougheloum, Harkat, Djeghaba (bib0023) 2015; 48 Quinlan (bib0037) 1993 Friedman, J. H., 1996. Another approach to polychotomous classification. Montgomery, Runger (bib0032) 2011 Nounou, Nounou, Meskin, Datta, Daugherty (bib0033) 2012; 9 Heo, S., Lee, J. H., 2018. Fault detection and classification using artificial neural networks. 10th IFAC Symposium on Advanced Control of Chemical Processes 51 (18), 470–475. Yin, Ding, Haghani, Hao, Zhang (bib0055) 2012; 22 Passino, Yurkovich (bib0035) 1998 Bay (bib0007) 1998 Basha, Nounou, Nounou (bib0004) 2018; 27 Reynolds, Lou, Lee, Wang (bib0040) 2013; 45 Yin, Ding, Zhang, Hagahni, Naik (bib0056) 2011; 44 (bib0045) 2012 Eslamloueyan (bib0015) 2011; 11 Jolliffe (bib0024) 2002 Vaart (bib0051) 1998 Rieth, C. A., Amsel, B. D., Tran, R., Cook, M. B., 2017. Additional Tennessee Eastman Process Simulation Data for Anomaly Detection Evaluation. 10.7910/DVN/6C3JR1 Chiang, Russell, Braatz (bib0011) 2001 Reynolds, Lou (bib0038) 2010; 42 Aly, M., 2005. Survey on multiclass classification methods. Sheriff, Botre, Mansouri, Nounou, Nounou, Karim (bib0047) 2017 Sheriff, Karim, Nounou, Nounou (bib0048) 2018; 27 Bathelt, Ricker, Jelali (bib0006) 2015; 48 Ku, Storer, Georgakis (bib0025) 1995; 30 Dietterich, Bakiri (bib0013) 1995; 39 Lauro, Palumbo (bib0026) 2000; 15 Russell, Chiang, Braatz (bib0044) 2000 Lv, Wen, Bao, Liu (bib0030) 2016 Palumbo, Lauro (bib0034) 2001 Wu, Zhao (bib0054) 2018; 115 (bib0046) 2012 Strang (bib0049) 2016 Reynolds, Lou (bib0039) 2012; 10 Benaicha, Mourot, Ragot, Benothman (bib0008) 2013; 1 Bathelt, Jelali (bib0005) 2014 Le-Rademacher (bib0029) 2008 MacGregor (bib0031) 1994; 27 Zhang (bib0057) 2009; 64 MacGregor (10.1016/j.compchemeng.2020.106786_bib0031) 1994; 27 Palumbo (10.1016/j.compchemeng.2020.106786_bib0034) 2001 Hastie (10.1016/j.compchemeng.2020.106786_bib0017) 1998; 26 Hekmati (10.1016/j.compchemeng.2020.106786_bib0018) 2010 Bay (10.1016/j.compchemeng.2020.106786_bib0007) 1998 Bathelt (10.1016/j.compchemeng.2020.106786_bib0005) 2014 Breiman (10.1016/j.compchemeng.2020.106786_bib0009) 1984; 40 Lv (10.1016/j.compchemeng.2020.106786_bib0030) 2016 Sheriff (10.1016/j.compchemeng.2020.106786_bib0047) 2017 Downs (10.1016/j.compchemeng.2020.106786_bib0014) 1993; 17 Strang (10.1016/j.compchemeng.2020.106786_bib0049) 2016 Zhang (10.1016/j.compchemeng.2020.106786_bib0058) 2017; 107 Heo (10.1016/j.compchemeng.2020.106786_bib0020) 2019; 7 Izem (10.1016/j.compchemeng.2020.106786_bib0023) 2015; 48 Quinlan (10.1016/j.compchemeng.2020.106786_bib0037) 1993 Zhang (10.1016/j.compchemeng.2020.106786_bib0057) 2009; 64 Lauro (10.1016/j.compchemeng.2020.106786_bib0026) 2000; 15 Reynolds (10.1016/j.compchemeng.2020.106786_bib0040) 2013; 45 10.1016/j.compchemeng.2020.106786_bib0041 Vaart (10.1016/j.compchemeng.2020.106786_bib0051) 1998 Reynolds (10.1016/j.compchemeng.2020.106786_bib0038) 2010; 42 (10.1016/j.compchemeng.2020.106786_bib0045) 2012 Tharrault (10.1016/j.compchemeng.2020.106786_bib0050) 2010 Dietterich (10.1016/j.compchemeng.2020.106786_bib0013) 1995; 39 Burges (10.1016/j.compchemeng.2020.106786_bib0010) 1998; 2 Lauro (10.1016/j.compchemeng.2020.106786_bib0028) 2008 Sheriff (10.1016/j.compchemeng.2020.106786_bib0048) 2018; 27 (10.1016/j.compchemeng.2020.106786_bib0046) 2012 10.1016/j.compchemeng.2020.106786_bib0002 Chiang (10.1016/j.compchemeng.2020.106786_bib0011) 2001 Ku (10.1016/j.compchemeng.2020.106786_bib0025) 1995; 30 Rifkin (10.1016/j.compchemeng.2020.106786_bib0042) 2004; 5 Woodall (10.1016/j.compchemeng.2020.106786_bib0053) 2005; 47 Benaicha (10.1016/j.compchemeng.2020.106786_bib0008) 2013; 1 Le-Rademacher (10.1016/j.compchemeng.2020.106786_bib0029) 2008 Rish (10.1016/j.compchemeng.2020.106786_bib0043) 2001; 3 Basha (10.1016/j.compchemeng.2020.106786_bib0004) 2018; 27 Hyvärinen (10.1016/j.compchemeng.2020.106786_bib0022) 2001; 26 Yin (10.1016/j.compchemeng.2020.106786_bib0056) 2011; 44 Eslamloueyan (10.1016/j.compchemeng.2020.106786_bib0015) 2011; 11 Reynolds (10.1016/j.compchemeng.2020.106786_bib0039) 2012; 10 Bathelt (10.1016/j.compchemeng.2020.106786_bib0006) 2015; 48 Lauro (10.1016/j.compchemeng.2020.106786_bib0027) 2005 10.1016/j.compchemeng.2020.106786_bib0019 Yin (10.1016/j.compchemeng.2020.106786_bib0055) 2012; 22 Passino (10.1016/j.compchemeng.2020.106786_bib0035) 1998 Nounou (10.1016/j.compchemeng.2020.106786_bib0033) 2012; 9 Basha (10.1016/j.compchemeng.2020.106786_bib0003) 2018 Montgomery (10.1016/j.compchemeng.2020.106786_bib0032) 2011 10.1016/j.compchemeng.2020.106786_bib0016 Jolliffe (10.1016/j.compchemeng.2020.106786_bib0024) 2002 Wang (10.1016/j.compchemeng.2020.106786_bib0052) 2013; 45 Qiu (10.1016/j.compchemeng.2020.106786_bib0036) 2013 Allwein (10.1016/j.compchemeng.2020.106786_bib0001) 2001; 1 Cortes (10.1016/j.compchemeng.2020.106786_bib0012) 1995; 20 Wu (10.1016/j.compchemeng.2020.106786_bib0054) 2018; 115 Russell (10.1016/j.compchemeng.2020.106786_sbref0040) 2000 |
| References_xml | – volume: 2 start-page: 121 year: 1998 end-page: 167 ident: bib0010 article-title: A tutorial on support vector machines for pattern recognition publication-title: Data Min. Knowl. Discov. – volume: 3 start-page: 41 year: 2001 end-page: 46 ident: bib0043 article-title: An empirical study of the naïve bayes classifier publication-title: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence – start-page: 37 year: 1998 end-page: 45 ident: bib0007 article-title: Combining nearest neighbor classifiers through multiple feature subsets publication-title: International Conference on Machine Learning – volume: 20 start-page: 273 year: 1995 end-page: 297 ident: bib0012 article-title: Support-vector networks publication-title: Mach. Learn. – volume: 1 start-page: 113 year: 2001 end-page: 141 ident: bib0001 article-title: Reducing multiclass to binary: a unifying approach for margin classifiers publication-title: J. Mach. Learn. Res. – start-page: 6851 year: 2016 end-page: 6856 ident: bib0030 article-title: Fault diagnosis based on deep learning publication-title: American Control Conference (ACC) – year: 1998 ident: bib0035 article-title: Fuzzy Control – volume: 48 start-page: 1402 year: 2015 end-page: 1407 ident: bib0023 article-title: Fault detection and isolation using interval principal component analysis methods publication-title: Int. Fed. Autom. Control – volume: 27 start-page: 227 year: 2018 end-page: 246 ident: bib0048 article-title: Process monitoring using PCA-based GLR methods: acomparative study publication-title: J. Comput. Sci. – year: 2016 ident: bib0049 article-title: Intro. to linear algebra – volume: 115 start-page: 185 year: 2018 end-page: 197 ident: bib0054 article-title: Deep convolutional neural network model based chemical process fault diagnosis publication-title: Comput. Chem. Eng. – year: 2012 ident: bib0045 publication-title: Principal Component Analysis: Engineering Applications – volume: 107 start-page: 395 year: 2017 end-page: 407 ident: bib0058 article-title: A deep belief network based fault diagnosis model for complex chemical processes publication-title: Comput. Chem. Eng. – volume: 45 start-page: 34 year: 2013 end-page: 60 ident: bib0040 article-title: The design of GLR control charts for monitoring the process mean and variance publication-title: J. Qual. Technol. – reference: Heo, S., Lee, J. H., 2018. Fault detection and classification using artificial neural networks. 10th IFAC Symposium on Advanced Control of Chemical Processes 51 (18), 470–475. – volume: 10 start-page: 3 year: 2012 end-page: 17 ident: bib0039 article-title: A GLR control chart for monitoring the process variance publication-title: Frontiers in Statistical Quality Control – volume: 5 start-page: 101 year: 2004 end-page: 141 ident: bib0042 article-title: In defense of one-vs-all classification publication-title: J. Mach. Learn. Res. – start-page: 99 year: 2000 end-page: 108 ident: bib0044 article-title: Tennessee Eastman Process – reference: Aly, M., 2005. Survey on multiclass classification methods. – year: 1998 ident: bib0051 article-title: Asymptotic Statistics – reference: Friedman, J. H., 1996. Another approach to polychotomous classification. – volume: 26 year: 2001 ident: bib0022 article-title: Independent Component Analysis – start-page: 237 year: 2017 end-page: 261 ident: bib0047 article-title: Process monitoring using data-based fault detection techniques: Comparative studies publication-title: Fault Diagnosis and Detection – year: 2008 ident: bib0029 publication-title: Principal Component Analysis for Interval-Valued and Histogram-Valued Data and Likelihood Functions and Some Maximum Likelihood Estimators for Symbolic Data – volume: 27 start-page: 427 year: 1994 end-page: 437 ident: bib0031 article-title: Statistical process control of multivariate processes publication-title: IFAC Symp. Adv. Control Chem. Process. – start-page: 1 year: 2014 end-page: 6 ident: bib0005 article-title: Comparative study of subspace identification methods on the tennessee eastman process under disturbance effects publication-title: 5th International Symposium on Advanced Control of Industrial Processes – volume: 17 start-page: 245 year: 1993 end-page: 255 ident: bib0014 article-title: A plant-wide industrial process control problem publication-title: Comput. Chem. Eng. – volume: 48 start-page: 309 year: 2015 end-page: 314 ident: bib0006 article-title: Revision of the tennessee eastman process model publication-title: 9th IFAC Symposium on Advanced Control of Chemical Processes – volume: 30 start-page: 179 year: 1995 end-page: 196 ident: bib0025 article-title: Disturbance detection and isolation by dynamic principal component analysis publication-title: Chemom. Intell. Lab. Syst. – year: 2001 ident: bib0011 article-title: Fault Detection and Diagnosis in Industrial Systems – year: 2011 ident: bib0032 article-title: Applied Statistics and Probability for Engineers – volume: 9 start-page: 1819 year: 2012 end-page: 1825 ident: bib0033 article-title: Fuzzy intervention in biological phenomena publication-title: IEEE/ACM Trans. Comput. Biol. Bioinf. – year: 2013 ident: bib0036 article-title: Introduction to Statistical Process Control – volume: 22 start-page: 1567 year: 2012 end-page: 1581 ident: bib0055 article-title: A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark tennessee eastman process publication-title: J. Process Control – year: 2012 ident: bib0046 publication-title: Principal Component Analysis: Multidisciplinary Applications – reference: Rieth, C. A., Amsel, B. D., Tran, R., Cook, M. B., 2017. Additional Tennessee Eastman Process Simulation Data for Anomaly Detection Evaluation. 10.7910/DVN/6C3JR1 – start-page: 173 year: 2005 end-page: 184 ident: bib0027 article-title: Principal component analysis for non-precise data publication-title: New Developments in Classification and Data Analysis – start-page: 369 year: 2010 end-page: 392 ident: bib0050 article-title: Sensor fault detection and isolation by robust principal component analysis publication-title: Fault Detection – volume: 42 start-page: 287 year: 2010 end-page: 310 ident: bib0038 article-title: An evaluation of a GLR control chart for monitoring the process mean publication-title: J. Qual. Technol. – volume: 44 start-page: 12389 year: 2011 end-page: 12394 ident: bib0056 article-title: Study on modifications of PLS approach for process monitoring publication-title: IFAC Proc. Volumes – volume: 40 start-page: 874 year: 1984 ident: bib0009 article-title: Classification and regression trees publication-title: Biometrics – volume: 11 start-page: 1407 year: 2011 end-page: 1415 ident: bib0015 article-title: Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee-Eastman process publication-title: Appl. Soft Comput. – volume: 1 start-page: 162 year: 2013 end-page: 167 ident: bib0008 article-title: Fault detection and isolation with interval principal component analysis publication-title: International Conference on Control, Engineering and Information Technology – volume: 64 start-page: 801 year: 2009 end-page: 811 ident: bib0057 article-title: Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM publication-title: Chem. Eng. Sci. – start-page: 641 year: 2001 end-page: 648 ident: bib0034 article-title: A PCA for interval-valued data based on midpoints and radii publication-title: New Dev. Psychom. – year: 2018 ident: bib0003 publication-title: Interval Principal Component Analysis and its Application to Fault Detection and Data Classification – year: 1993 ident: bib0037 article-title: C4.5: Programs for Machine Learning – volume: 26 start-page: 451 year: 1998 end-page: 471 ident: bib0017 article-title: Classification by pairwise coupling publication-title: Ann. Stat. – volume: 39 start-page: 1 year: 1995 end-page: 38 ident: bib0013 article-title: Solving multiclass learning problems via error-correcting output codes publication-title: J. Artif. Intell. Res. – volume: 15 start-page: 73 year: 2000 end-page: 87 ident: bib0026 article-title: Principal component analysis of interval data: a symbolic data analysis publication-title: Comput. Stat. – start-page: 279 year: 2008 end-page: 311 ident: bib0028 article-title: Principal component analysis of symbolic data described by intervals publication-title: Symb. Data Anal. SODAS Softw. – volume: 7 start-page: 411 year: 2019 ident: bib0020 article-title: Statistical process monitoring of the tennessee eastman process using parallel autoassociative neural networks and a large dataset publication-title: Processes – start-page: 362 year: 2010 end-page: 367 ident: bib0018 article-title: Fault detection and isolation of tennessee eastman process using improved RBF network by genetic algorithm publication-title: 8th European Workshop on Advanced Control and Diagnosis – volume: 27 start-page: 1 year: 2018 end-page: 9 ident: bib0004 article-title: Multivariate fault detection and classification using interval principal component analysis publication-title: J. Comput. Sci. – volume: 47 start-page: 425 year: 2005 end-page: 436 ident: bib0053 article-title: The inertial properties of quality control charts publication-title: Technometrics – volume: 45 start-page: 18 year: 2013 end-page: 33 ident: bib0052 article-title: A GLR control chart for monitoring the mean vector of a multivariate normal process publication-title: J. Qual. Technol. – year: 2002 ident: bib0024 article-title: Principal component analysis – start-page: 641 year: 2001 ident: 10.1016/j.compchemeng.2020.106786_bib0034 article-title: A PCA for interval-valued data based on midpoints and radii publication-title: New Dev. Psychom. – volume: 10 start-page: 3 year: 2012 ident: 10.1016/j.compchemeng.2020.106786_bib0039 article-title: A GLR control chart for monitoring the process variance – start-page: 99 year: 2000 ident: 10.1016/j.compchemeng.2020.106786_sbref0040 – volume: 9 start-page: 1819 issue: 1 year: 2012 ident: 10.1016/j.compchemeng.2020.106786_bib0033 article-title: Fuzzy intervention in biological phenomena publication-title: IEEE/ACM Trans. Comput. Biol. Bioinf. doi: 10.1109/TCBB.2012.113 – year: 2018 ident: 10.1016/j.compchemeng.2020.106786_bib0003 – start-page: 1 year: 2014 ident: 10.1016/j.compchemeng.2020.106786_bib0005 article-title: Comparative study of subspace identification methods on the tennessee eastman process under disturbance effects – volume: 64 start-page: 801 issue: 5 year: 2009 ident: 10.1016/j.compchemeng.2020.106786_bib0057 article-title: Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM publication-title: Chem. Eng. Sci. doi: 10.1016/j.ces.2008.10.012 – volume: 7 start-page: 411 issue: 7 year: 2019 ident: 10.1016/j.compchemeng.2020.106786_bib0020 article-title: Statistical process monitoring of the tennessee eastman process using parallel autoassociative neural networks and a large dataset publication-title: Processes doi: 10.3390/pr7070411 – start-page: 173 year: 2005 ident: 10.1016/j.compchemeng.2020.106786_bib0027 article-title: Principal component analysis for non-precise data publication-title: New Developments in Classification and Data Analysis doi: 10.1007/3-540-27373-5_21 – volume: 42 start-page: 287 year: 2010 ident: 10.1016/j.compchemeng.2020.106786_bib0038 article-title: An evaluation of a GLR control chart for monitoring the process mean publication-title: J. Qual. Technol. doi: 10.1080/00224065.2010.11917825 – volume: 47 start-page: 425 issue: 4 year: 2005 ident: 10.1016/j.compchemeng.2020.106786_bib0053 article-title: The inertial properties of quality control charts publication-title: Technometrics doi: 10.1198/004017005000000256 – volume: 27 start-page: 1 year: 2018 ident: 10.1016/j.compchemeng.2020.106786_bib0004 article-title: Multivariate fault detection and classification using interval principal component analysis publication-title: J. Comput. Sci. doi: 10.1016/j.jocs.2018.04.017 – volume: 11 start-page: 1407 issue: 1 year: 2011 ident: 10.1016/j.compchemeng.2020.106786_bib0015 article-title: Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee-Eastman process publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2010.04.012 – volume: 30 start-page: 179 issue: 1 year: 1995 ident: 10.1016/j.compchemeng.2020.106786_bib0025 article-title: Disturbance detection and isolation by dynamic principal component analysis publication-title: Chemom. Intell. Lab. Syst. doi: 10.1016/0169-7439(95)00076-3 – volume: 44 start-page: 12389 issue: 1 year: 2011 ident: 10.1016/j.compchemeng.2020.106786_bib0056 article-title: Study on modifications of PLS approach for process monitoring publication-title: IFAC Proc. Volumes doi: 10.3182/20110828-6-IT-1002.02876 – start-page: 369 year: 2010 ident: 10.1016/j.compchemeng.2020.106786_bib0050 article-title: Sensor fault detection and isolation by robust principal component analysis – volume: 17 start-page: 245 year: 1993 ident: 10.1016/j.compchemeng.2020.106786_bib0014 article-title: A plant-wide industrial process control problem publication-title: Comput. Chem. Eng. doi: 10.1016/0098-1354(93)80018-I – year: 2012 ident: 10.1016/j.compchemeng.2020.106786_bib0046 – volume: 26 start-page: 451 year: 1998 ident: 10.1016/j.compchemeng.2020.106786_bib0017 article-title: Classification by pairwise coupling publication-title: Ann. Stat. doi: 10.1214/aos/1028144844 – volume: 20 start-page: 273 year: 1995 ident: 10.1016/j.compchemeng.2020.106786_bib0012 article-title: Support-vector networks publication-title: Mach. Learn. doi: 10.1023/A:1022627411411 – volume: 27 start-page: 427 issue: 2 year: 1994 ident: 10.1016/j.compchemeng.2020.106786_bib0031 article-title: Statistical process control of multivariate processes publication-title: IFAC Symp. Adv. Control Chem. Process. – volume: 45 start-page: 18 year: 2013 ident: 10.1016/j.compchemeng.2020.106786_bib0052 article-title: A GLR control chart for monitoring the mean vector of a multivariate normal process publication-title: J. Qual. Technol. doi: 10.1080/00224065.2013.11917913 – volume: 2 start-page: 121 year: 1998 ident: 10.1016/j.compchemeng.2020.106786_bib0010 article-title: A tutorial on support vector machines for pattern recognition publication-title: Data Min. Knowl. Discov. doi: 10.1023/A:1009715923555 – volume: 48 start-page: 309 year: 2015 ident: 10.1016/j.compchemeng.2020.106786_bib0006 article-title: Revision of the tennessee eastman process model – volume: 3 start-page: 41 year: 2001 ident: 10.1016/j.compchemeng.2020.106786_bib0043 article-title: An empirical study of the naïve bayes classifier – year: 1993 ident: 10.1016/j.compchemeng.2020.106786_bib0037 – volume: 1 start-page: 162 year: 2013 ident: 10.1016/j.compchemeng.2020.106786_bib0008 article-title: Fault detection and isolation with interval principal component analysis – volume: 115 start-page: 185 year: 2018 ident: 10.1016/j.compchemeng.2020.106786_bib0054 article-title: Deep convolutional neural network model based chemical process fault diagnosis publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2018.04.009 – volume: 1 start-page: 113 year: 2001 ident: 10.1016/j.compchemeng.2020.106786_bib0001 article-title: Reducing multiclass to binary: a unifying approach for margin classifiers publication-title: J. Mach. Learn. Res. – volume: 27 start-page: 227 year: 2018 ident: 10.1016/j.compchemeng.2020.106786_bib0048 article-title: Process monitoring using PCA-based GLR methods: acomparative study publication-title: J. Comput. Sci. doi: 10.1016/j.jocs.2018.05.013 – year: 2011 ident: 10.1016/j.compchemeng.2020.106786_bib0032 – ident: 10.1016/j.compchemeng.2020.106786_bib0019 doi: 10.1016/j.ifacol.2018.09.380 – start-page: 279 year: 2008 ident: 10.1016/j.compchemeng.2020.106786_bib0028 article-title: Principal component analysis of symbolic data described by intervals publication-title: Symb. Data Anal. SODAS Softw. – ident: 10.1016/j.compchemeng.2020.106786_bib0041 – volume: 5 start-page: 101 year: 2004 ident: 10.1016/j.compchemeng.2020.106786_bib0042 article-title: In defense of one-vs-all classification publication-title: J. Mach. Learn. Res. – volume: 22 start-page: 1567 year: 2012 ident: 10.1016/j.compchemeng.2020.106786_bib0055 article-title: A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark tennessee eastman process publication-title: J. Process Control doi: 10.1016/j.jprocont.2012.06.009 – year: 2013 ident: 10.1016/j.compchemeng.2020.106786_bib0036 – volume: 40 start-page: 874 issue: 3 year: 1984 ident: 10.1016/j.compchemeng.2020.106786_bib0009 article-title: Classification and regression trees publication-title: Biometrics doi: 10.2307/2530946 – start-page: 237 year: 2017 ident: 10.1016/j.compchemeng.2020.106786_bib0047 article-title: Process monitoring using data-based fault detection techniques: Comparative studies – year: 2012 ident: 10.1016/j.compchemeng.2020.106786_bib0045 – ident: 10.1016/j.compchemeng.2020.106786_bib0016 – year: 1998 ident: 10.1016/j.compchemeng.2020.106786_bib0051 – volume: 26 year: 2001 ident: 10.1016/j.compchemeng.2020.106786_bib0022 – year: 2001 ident: 10.1016/j.compchemeng.2020.106786_bib0011 – start-page: 37 year: 1998 ident: 10.1016/j.compchemeng.2020.106786_bib0007 article-title: Combining nearest neighbor classifiers through multiple feature subsets – year: 2008 ident: 10.1016/j.compchemeng.2020.106786_bib0029 – ident: 10.1016/j.compchemeng.2020.106786_bib0002 – year: 2016 ident: 10.1016/j.compchemeng.2020.106786_bib0049 – volume: 39 start-page: 1 year: 1995 ident: 10.1016/j.compchemeng.2020.106786_bib0013 article-title: Solving multiclass learning problems via error-correcting output codes publication-title: J. Artif. Intell. Res. – volume: 15 start-page: 73 year: 2000 ident: 10.1016/j.compchemeng.2020.106786_bib0026 article-title: Principal component analysis of interval data: a symbolic data analysis publication-title: Comput. Stat. doi: 10.1007/s001800050038 – volume: 107 start-page: 395 year: 2017 ident: 10.1016/j.compchemeng.2020.106786_bib0058 article-title: A deep belief network based fault diagnosis model for complex chemical processes publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2017.02.041 – volume: 48 start-page: 1402 issue: 21 year: 2015 ident: 10.1016/j.compchemeng.2020.106786_bib0023 article-title: Fault detection and isolation using interval principal component analysis methods publication-title: Int. Fed. Autom. Control – start-page: 362 year: 2010 ident: 10.1016/j.compchemeng.2020.106786_bib0018 article-title: Fault detection and isolation of tennessee eastman process using improved RBF network by genetic algorithm – year: 1998 ident: 10.1016/j.compchemeng.2020.106786_bib0035 – volume: 45 start-page: 34 year: 2013 ident: 10.1016/j.compchemeng.2020.106786_bib0040 article-title: The design of GLR control charts for monitoring the process mean and variance publication-title: J. Qual. Technol. doi: 10.1080/00224065.2013.11917914 – year: 2002 ident: 10.1016/j.compchemeng.2020.106786_bib0024 – start-page: 6851 year: 2016 ident: 10.1016/j.compchemeng.2020.106786_bib0030 article-title: Fault diagnosis based on deep learning |
| SSID | ssj0002488 |
| Score | 2.4106688 |
| Snippet | Multiclass classification of big data is a subject of broad interest in machine learning research nowadays, where it is necessary to extract important features... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 106786 |
| SubjectTerms | Binary decomposition Fault detection Generalized likelihood ratio test Hypothesis testing Interval aggregation Moving-window Multiclass classification Principal component analysis |
| Title | Multiclass data classification using fault detection-based techniques |
| URI | https://dx.doi.org/10.1016/j.compchemeng.2020.106786 |
| Volume | 136 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Baden-Württemberg Complete Freedom Collection (Elsevier) customDbUrl: eissn: 1873-4375 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0002488 issn: 0098-1354 databaseCode: GBLVA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier - Freedom Collection customDbUrl: eissn: 1873-4375 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0002488 issn: 0098-1354 databaseCode: ACRLP dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier ScienceDirect customDbUrl: eissn: 1873-4375 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0002488 issn: 0098-1354 databaseCode: .~1 dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier ScienceDirect Freedom Collection customDbUrl: eissn: 1873-4375 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0002488 issn: 0098-1354 databaseCode: AIKHN dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVLSH databaseName: Elsevier Journals customDbUrl: mediaType: online eissn: 1873-4375 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0002488 issn: 0098-1354 databaseCode: AKRWK dateStart: 19770101 isFulltext: true providerName: Library Specific Holdings |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dS8MwEA9jguiD-InzY0Twta5N0zQFX8bYmIp7crC3kibpmGgdWl_9271L2zlBUPAtOXJQfhz3UX53R8ilNL7NfB14UicCCpQk8qAOMZ5OjAkhgCvp_kPeT8R4ym9n0axFBk0vDNIqa99f-XTnrWtJr0azt1wssMc3kUEYcQZ2Cmes2zmPcYvB1ccXzYNxKZu5mfh6k1x8cbyQtg3YPNtiDqUiQzk4b_FzjFqLO6NdslMnjLRffdMeadlin2yvjRE8IEPXRasxD6bI-KTuiBwgBztFbvuc5gpeUWNLR74qPIxfhq5muL4dkulo-DAYe_V6BE-HLCg9YaUvrOCxH1gAObFM-QryNaWVynOQQjHFRKZjEwnDhQ5syGMtIxZHQWyTKDwi7eKlsMeEQt4VcpGJPBOKK4bMfK5lBrlVYKW1SYfIBpBU17PDcYXFU9qQxB7TNSxTxDKtsOwQtlJdVgM0_qJ03aCefrOGFBz97-on_1M_JVt4c7RGeUba5eu7PYfUo8y6zra6ZKN_czeefAI8S9oX |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dS8MwEA9DwY8H8RPnZwRf69o0TVPwRcbG1G1PG-wtpEk6JlqH1lf_di9pu00QFHwL1xyUH8fd_cIvF4SuufZN6qvA4yphQFCSyAMeoj2VaB1CAZfcnUMOhqw3pg-TaNJA7foujJVVVrm_zOkuW1eWVoVmaz6b2Tu-CQ_CiBKIU1gDb1-nEYktA7v5XOo8COW8Hpxpt2-gq6XIy-q2AZwXk0-BKxJrh-zNfi5SK4Wnu4t2qo4R35U_tYcaJt9H2ytzBA9Qx12jVbYRxlbyid3SioAc7tiK26c4k7ALa1M49VXu2QKm8WKI6_shGnc7o3bPq95H8FRIgsJjhvvMMBr7gQGUE0OkL6Fhk0rKLAMrsCnCUhXriGnKVGBCGisOGEVBbJIoPEJr-WtujhGGxiukLGVZyiSVxErzqeIpNFeB4cYkTcRrQISqhofbNyyeRa0SexIrWAqLpSixbCKycJ2XEzT-4nRboy6-hYOATP-7-8n_3C_RZm806Iv-_fDxFG3ZL07jyM_QWvH2Yc6hDynSCxdnX2h526w |
| 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=Multiclass+data+classification+using+fault+detection-based+techniques&rft.jtitle=Computers+%26+chemical+engineering&rft.au=Basha%2C+Nour&rft.au=Ziyan+Sheriff%2C+M.&rft.au=Kravaris%2C+Costas&rft.au=Nounou%2C+Hazem&rft.date=2020-05-08&rft.pub=Elsevier+Ltd&rft.issn=0098-1354&rft.eissn=1873-4375&rft.volume=136&rft_id=info:doi/10.1016%2Fj.compchemeng.2020.106786&rft.externalDocID=S0098135420300090 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0098-1354&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0098-1354&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0098-1354&client=summon |