A new fault diagnosis approach for induction motor using negative selection algorithm and its real-time implementation on FPGA
暂无分享,去创建一个
Mehmet Karaköse | Erhan Akin | Ilhan Aydin | Ebru Karakose | E. Akin | Mehmet Karaköse | I. Aydin | E. Karakose
[1] Mehmet Karakose,et al. An approach for automated fault diagnosis based on a fuzzy decision tree and boundary analysis of a reconstructed phase space. , 2014, ISA transactions.
[2] P. J. Costa Branco,et al. An artificial immune system approach for fault detection in the stator and rotor circuits of induction machines , 2011 .
[3] Myeongsu Kang,et al. An FPGA-Based Multicore System for Real-Time Bearing Fault Diagnosis Using Ultrasampling Rate AE Signals , 2015, IEEE Transactions on Industrial Electronics.
[4] Sy-Ruen Huang,et al. Fault analysis and diagnosis system for induction motors , 2016, Comput. Electr. Eng..
[5] Imre M. Jánosi,et al. Book Review: "Nonlinear Time Series Analysis, 2nd Edition" by Holger Kantz and Thomas Schreiber , 2004 .
[6] Mehmet Karakose,et al. FPGA based intelligent condition monitoring of induction motors: Detection, diagnosis, and prognosis , 2011, 2011 IEEE International Conference on Industrial Technology.
[7] Eric Monmasson,et al. FPGAs in Industrial Control Applications , 2011, IEEE Transactions on Industrial Informatics.
[8] Thomas G. Habetler,et al. A survey of condition monitoring and protection methods for medium voltage induction motors , 2009, 2009 IEEE Energy Conversion Congress and Exposition.
[9] Jonathan Timmis,et al. Artificial Immune Systems: A New Computational Intelligence Approach , 2003 .
[10] Zhou Ji,et al. V-detector: An efficient negative selection algorithm with "probably adequate" detector coverage , 2009, Inf. Sci..
[11] Richard J. Povinelli,et al. Time series classification using Gaussian mixture models of reconstructed phase spaces , 2004, IEEE Transactions on Knowledge and Data Engineering.
[12] Fernando José Von Zuben,et al. Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..
[13] Yuan Chen,et al. An iterative real-time nonlinear electromagnetic transient solver on FPGA , 2011, 2011 IEEE Power and Energy Society General Meeting.
[14] T. S. Radwan,et al. Real-Time Implementation of Wavelet Packet Transform-Based Diagnosis and Protection of Three-Phase Induction Motors , 2007, IEEE Transactions on Energy Conversion.
[15] Ting Yang,et al. Feature Knowledge Based Fault Detection of Induction Motors Through the Analysis of Stator Current Data , 2016, IEEE Transactions on Instrumentation and Measurement.
[16] F. Takens. Detecting strange attractors in turbulence , 1981 .
[17] Hamid-Reza Bahrami,et al. Iterative Condition Monitoring and Fault Diagnosis Scheme of Electric Motor for Harsh Industrial Application , 2015, IEEE Transactions on Industrial Electronics.
[18] Mehmet Karaköse,et al. A multi-objective artificial immune algorithm for parameter optimization in support vector machine , 2011, Appl. Soft Comput..
[19] Shen Yin,et al. An Intelligent Actuator Fault Reconstruction Scheme for Robotic Manipulators , 2018, IEEE Transactions on Cybernetics.
[20] M. Karakose,et al. Artificial immune inspired fault detection algorithm based on fuzzy clustering and genetic algorithm methods , 2008, 2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications.
[21] N. A. O. Demerdash,et al. Diagnostics of Bar and End-Ring Connector Breakage Faults in Polyphase Induction Motors through a Novel Dual Track of Time-Series Data Mining and Time-Stepping Coupled FE-State Space Modeling , 2002, IEEE Power Engineering Review.
[22] Jr. S. Marple,et al. Computing the discrete-time 'analytic' signal via FFT , 1999, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).
[23] Zheng Chen,et al. A new diagnosis of broken rotor bar fault extent in three phase squirrel cage induction motor , 2014 .
[24] Minqiang Xu,et al. Hierarchical fuzzy entropy and improved support vector machine based binary tree approach for rolling bearing fault diagnosis , 2016 .
[25] Hirotada Ohashi,et al. A negative selection algorithm for classification and reduction of the noise effect , 2009, Appl. Soft Comput..
[26] Richard J. Povinelli,et al. Induction Machine Broken Bar and Stator Short-Circuit Fault Diagnostics Based on Three-Phase Stator Current Envelopes , 2008, IEEE Transactions on Industrial Electronics.
[27] Mehmet Karaköse,et al. Artificial immune classifier with swarm learning , 2010, Eng. Appl. Artif. Intell..
[28] Arezki Menacer,et al. Fast Fourier and discrete wavelet transforms applied to sensorless vector control induction motor for rotor bar faults diagnosis. , 2014, ISA transactions.
[29] Fabio A. González,et al. Anomaly Detection Using Real-Valued Negative Selection , 2003, Genetic Programming and Evolvable Machines.
[30] Richard J. Povinelli,et al. Rotor Bar Fault Monitoring Method Based on Analysis of Air-Gap Torques of Induction Motors , 2013, IEEE Transactions on Industrial Informatics.
[31] Gianluca Ippoliti,et al. Electric Motor Fault Detection and Diagnosis by Kernel Density Estimation and Kullback–Leibler Divergence Based on Stator Current Measurements , 2015, IEEE Transactions on Industrial Electronics.
[32] Hayde Peregrina-Barreto,et al. FPGA-Based Broken Bars Detection on Induction Motors Under Different Load Using Motor Current Signature Analysis and Mathematical Morphology , 2014, IEEE Transactions on Instrumentation and Measurement.
[33] Arturo Garcia-Perez,et al. Novel hardware processing unit for dynamic on-line entropy estimation of discrete time information , 2010, Digit. Signal Process..
[34] A. Khezzar,et al. Instantaneous power spectrum analysis for broken bar fault detection in inverter-fed six-phase squirrel cage induction motor , 2014 .
[35] Gérard-André Capolino,et al. Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art , 2015, IEEE Transactions on Industrial Electronics.