Fault Diagnosis of Tennessee-Eastman Process Using Orthogonal Incremental Extreme Learning Machine Based on Driving Amount. Zou, W., Xia, Y., & Li, H. IEEE Transactions on Cybernetics, 48(12):3403–3410, December, 2018. Conference Name: IEEE Transactions on Cyberneticsdoi abstract bibtex Fault diagnosis is important to the industrial process. This paper proposes an orthogonal incremental extreme learning machine based on driving amount (DAOI-ELM) for recognizing the faults of the Tennessee-Eastman process (TEP). The basic idea of DAOI-ELM is to incorporate the Gram-Schmidt orthogonalization method and driving amount into an incremental extreme learning machine (I-ELM). The case study for the 2-D nonlinear function and regression problems from the UCI dataset results show that DAOI-ELM can obtain better generalization ability and a more compact structure of ELM than I-ELM, convex I-ELM (CI-ELM), orthogonal I-ELM (OI-ELM), and bidirectional ELM. The experimental training and testing data are derived from the simulations of TEP. The performance of DAOI-ELM is evaluated and compared with that of the back propagation neural network, support vector machine, I-ELM, CI-ELM, and OI-ELM. The simulation results show that DAOI-ELM diagnoses the TEP faults better than other methods.
@article{zou_fault_2018,
title = {Fault {Diagnosis} of {Tennessee}-{Eastman} {Process} {Using} {Orthogonal} {Incremental} {Extreme} {Learning} {Machine} {Based} on {Driving} {Amount}},
volume = {48},
issn = {2168-2275},
doi = {10.1109/TCYB.2018.2830338},
abstract = {Fault diagnosis is important to the industrial process. This paper proposes an orthogonal incremental extreme learning machine based on driving amount (DAOI-ELM) for recognizing the faults of the Tennessee-Eastman process (TEP). The basic idea of DAOI-ELM is to incorporate the Gram-Schmidt orthogonalization method and driving amount into an incremental extreme learning machine (I-ELM). The case study for the 2-D nonlinear function and regression problems from the UCI dataset results show that DAOI-ELM can obtain better generalization ability and a more compact structure of ELM than I-ELM, convex I-ELM (CI-ELM), orthogonal I-ELM (OI-ELM), and bidirectional ELM. The experimental training and testing data are derived from the simulations of TEP. The performance of DAOI-ELM is evaluated and compared with that of the back propagation neural network, support vector machine, I-ELM, CI-ELM, and OI-ELM. The simulation results show that DAOI-ELM diagnoses the TEP faults better than other methods.},
number = {12},
journal = {IEEE Transactions on Cybernetics},
author = {Zou, Weidong and Xia, Yuanqing and Li, Huifang},
month = dec,
year = {2018},
note = {Conference Name: IEEE Transactions on Cybernetics},
keywords = {Artificial neural networks, Driving amount, Fault diagnosis, Gram–Schmidt orthogonalization method, Network architecture, Predictive maintenance, Prognostics and health management, Tennessee-Eastman process (TEP), fault diagnosis},
pages = {3403--3410},
}
Downloads: 0
{"_id":"Y3T2sDsywAW2WBiQC","bibbaseid":"zou-xia-li-faultdiagnosisoftennesseeeastmanprocessusingorthogonalincrementalextremelearningmachinebasedondrivingamount-2018","author_short":["Zou, W.","Xia, Y.","Li, H."],"bibdata":{"bibtype":"article","type":"article","title":"Fault Diagnosis of Tennessee-Eastman Process Using Orthogonal Incremental Extreme Learning Machine Based on Driving Amount","volume":"48","issn":"2168-2275","doi":"10.1109/TCYB.2018.2830338","abstract":"Fault diagnosis is important to the industrial process. This paper proposes an orthogonal incremental extreme learning machine based on driving amount (DAOI-ELM) for recognizing the faults of the Tennessee-Eastman process (TEP). The basic idea of DAOI-ELM is to incorporate the Gram-Schmidt orthogonalization method and driving amount into an incremental extreme learning machine (I-ELM). The case study for the 2-D nonlinear function and regression problems from the UCI dataset results show that DAOI-ELM can obtain better generalization ability and a more compact structure of ELM than I-ELM, convex I-ELM (CI-ELM), orthogonal I-ELM (OI-ELM), and bidirectional ELM. The experimental training and testing data are derived from the simulations of TEP. The performance of DAOI-ELM is evaluated and compared with that of the back propagation neural network, support vector machine, I-ELM, CI-ELM, and OI-ELM. The simulation results show that DAOI-ELM diagnoses the TEP faults better than other methods.","number":"12","journal":"IEEE Transactions on Cybernetics","author":[{"propositions":[],"lastnames":["Zou"],"firstnames":["Weidong"],"suffixes":[]},{"propositions":[],"lastnames":["Xia"],"firstnames":["Yuanqing"],"suffixes":[]},{"propositions":[],"lastnames":["Li"],"firstnames":["Huifang"],"suffixes":[]}],"month":"December","year":"2018","note":"Conference Name: IEEE Transactions on Cybernetics","keywords":"Artificial neural networks, Driving amount, Fault diagnosis, Gram–Schmidt orthogonalization method, Network architecture, Predictive maintenance, Prognostics and health management, Tennessee-Eastman process (TEP), fault diagnosis","pages":"3403–3410","bibtex":"@article{zou_fault_2018,\n\ttitle = {Fault {Diagnosis} of {Tennessee}-{Eastman} {Process} {Using} {Orthogonal} {Incremental} {Extreme} {Learning} {Machine} {Based} on {Driving} {Amount}},\n\tvolume = {48},\n\tissn = {2168-2275},\n\tdoi = {10.1109/TCYB.2018.2830338},\n\tabstract = {Fault diagnosis is important to the industrial process. This paper proposes an orthogonal incremental extreme learning machine based on driving amount (DAOI-ELM) for recognizing the faults of the Tennessee-Eastman process (TEP). The basic idea of DAOI-ELM is to incorporate the Gram-Schmidt orthogonalization method and driving amount into an incremental extreme learning machine (I-ELM). The case study for the 2-D nonlinear function and regression problems from the UCI dataset results show that DAOI-ELM can obtain better generalization ability and a more compact structure of ELM than I-ELM, convex I-ELM (CI-ELM), orthogonal I-ELM (OI-ELM), and bidirectional ELM. The experimental training and testing data are derived from the simulations of TEP. The performance of DAOI-ELM is evaluated and compared with that of the back propagation neural network, support vector machine, I-ELM, CI-ELM, and OI-ELM. The simulation results show that DAOI-ELM diagnoses the TEP faults better than other methods.},\n\tnumber = {12},\n\tjournal = {IEEE Transactions on Cybernetics},\n\tauthor = {Zou, Weidong and Xia, Yuanqing and Li, Huifang},\n\tmonth = dec,\n\tyear = {2018},\n\tnote = {Conference Name: IEEE Transactions on Cybernetics},\n\tkeywords = {Artificial neural networks, Driving amount, Fault diagnosis, Gram–Schmidt orthogonalization method, Network architecture, Predictive maintenance, Prognostics and health management, Tennessee-Eastman process (TEP), fault diagnosis},\n\tpages = {3403--3410},\n}\n\n\n\n","author_short":["Zou, W.","Xia, Y.","Li, H."],"key":"zou_fault_2018","id":"zou_fault_2018","bibbaseid":"zou-xia-li-faultdiagnosisoftennesseeeastmanprocessusingorthogonalincrementalextremelearningmachinebasedondrivingamount-2018","role":"author","urls":{},"keyword":["Artificial neural networks","Driving amount","Fault diagnosis","Gram–Schmidt orthogonalization method","Network architecture","Predictive maintenance","Prognostics and health management","Tennessee-Eastman process (TEP)","fault diagnosis"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":["artificial neural networks","driving amount","fault diagnosis","gram–schmidt orthogonalization method","network architecture","predictive maintenance","prognostics and health management","tennessee-eastman process (tep)","fault diagnosis"],"search_terms":["fault","diagnosis","tennessee","eastman","process","using","orthogonal","incremental","extreme","learning","machine","based","driving","amount","zou","xia","li"],"title":"Fault Diagnosis of Tennessee-Eastman Process Using Orthogonal Incremental Extreme Learning Machine Based on Driving Amount","year":2018}