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},
}
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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. 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