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Performance analysis of extreme learning machine for automatic diagnosis of electrical submersible pump conditions.

, , , and . INDIN, page 67-72. IEEE, (2014)

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Feature models and condition visualization for rotating machinery fault diagnosis., , , and . ICECS, page 265-268. IEEE, (2013)Motor Pump Fault Diagnosis with Feature Selection and Levenberg-Marquardt Trained Feedforward Neural Network., and . CAIP (1), volume 8047 of Lecture Notes in Computer Science, page 449-456. Springer, (2013)Multi-label Fault Classification Experiments in a Chemical Process., , , and . BRACIS, page 265-270. IEEE Computer Society, (2014)Learning of Specific Process Monitors in Machine Tool Supervision., , and . AIRTC, page 105-110. Elsevier, (1994)Performance analysis of extreme learning machine for automatic diagnosis of electrical submersible pump conditions., , , and . INDIN, page 67-72. IEEE, (2014)An Overproduce-and-Choose Strategy to Create Classifier Ensembles with Tuned SVM Parameters Applied to Real-World Fault Diagnosis., , and . CIARP, volume 6419 of Lecture Notes in Computer Science, page 500-508. Springer, (2010)Cascade Feature Selection and ELM for automatic fault diagnosis of the Tennessee Eastman process., , and . Neurocomputing, (2017)Evaluation of the Extreme Learning Machine for automatic fault diagnosis of the Tennessee Eastman chemical process., , and . IECON, page 2551-2557. IEEE, (2014)Feature selection for multivariate contribution analysis in fault detection and isolation., , and . J. Frankl. Inst., 357 (10): 6294-6320 (2020)Combining classifiers with decision templates for automatic fault diagnosis of electrical submersible pumps., , , , , and . Integr. Comput. Aided Eng., 25 (4): 381-396 (2018)