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Cascade Feature Selection and ELM for automatic fault diagnosis of the Tennessee Eastman process.

, , and . Neurocomputing, (2017)

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Computational intelligence for automatic diagnosis of submersible motor pump conditions in offshore oil exploration., , , and . ICECS, page 477-480. IEEE, (2013)Feature models and condition visualization for rotating machinery fault diagnosis., , , and . ICECS, page 265-268. IEEE, (2013)Fast feature selection using hybrid ranking and wrapper approach for automatic fault diagnosis of motorpumps based on vibration signals., , , and . INDIN, page 127-132. IEEE, (2015)Heterogeneous Feature Models and Feature Selection Applied to Bearing Fault Diagnosis., , and . IEEE Trans. Ind. Electron., 62 (1): 637-646 (2015)Binary feature selection classifier ensemble for fault diagnosis of submersible motor pump., , , , , and . ISIE, page 1807-1812. IEEE, (2017)Kernel and random extreme learning machine applied to submersible motor pump fault diagnosis., , , , , and . IJCNN, page 3347-3354. IEEE, (2017)An experimental methodology to evaluate machine learning methods for fault diagnosis based on vibration signals., , , , and . Expert Syst. Appl., (2021)A Proposal to Mitigate Similarity Bias for the Paderborn Bearing Data Set., and . IECON, page 1-6. IEEE, (2021)Performance analysis of extreme learning machine for automatic diagnosis of electrical submersible pump conditions., , , and . INDIN, page 67-72. IEEE, (2014)Evaluation of the Extreme Learning Machine for automatic fault diagnosis of the Tennessee Eastman chemical process., , and . IECON, page 2551-2557. IEEE, (2014)