Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

[1]  Hanbo Zheng,et al.  A novel model based on wavelet LS-SVM integrated improved PSO algorithm for forecasting of dissolved gas contents in power transformers , 2018 .

[2]  Dongming Zhao,et al.  Oil-immersed Transformer Internal Thermoelectric Potential Fault Diagnosis Based on Decision-tree of KNIME Platform , 2016, ANT/SEIT.

[3]  Antero Arkkio,et al.  Detection of stator winding fault in induction motor using fuzzy logic , 2008, Appl. Soft Comput..

[4]  Blaz Zupan,et al.  Predictive data mining in clinical medicine: Current issues and guidelines , 2008, Int. J. Medical Informatics.

[5]  V. Fernão Pires,et al.  Unsupervised Neural-Network-Based Algorithm for an On-Line Diagnosis of Three-Phase Induction Motor Stator Fault , 2007, IEEE Transactions on Industrial Electronics.

[6]  Fiorella Lauro,et al.  Building Fan Coil Electric Consumption Analysis with Fuzzy Approaches for Fault Detection and Diagnosis , 2014 .

[7]  Amir H. Payberah,et al.  An adaptive algorithm for anomaly and novelty detection in evolving data streams , 2018, Data Mining and Knowledge Discovery.

[8]  Rene de Jesus Romero-Troncoso,et al.  Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors , 2014, TheScientificWorldJournal.

[9]  Zhiqiang Ge,et al.  Data Mining and Analytics in the Process Industry: The Role of Machine Learning , 2017, IEEE Access.

[10]  Emilio Celotto,et al.  Visualizing the behavior and some symmetry properties of Bayesian confirmation measures , 2017, Data Mining and Knowledge Discovery.

[11]  Arturo Garcia-Perez,et al.  Detection and Classification of Single and Combined Power Quality Disturbances Using Neural Networks , 2014, IEEE Transactions on Industrial Electronics.

[12]  Rudra Prakash Maheshwari,et al.  Fault classification technique for series compensated transmission line using support vector machine , 2010 .

[13]  Shakeb A. Khan,et al.  A comprehensive comparative study of DGA based transformer fault diagnosis using fuzzy logic and ANFIS models , 2015, IEEE Transactions on Dielectrics and Electrical Insulation.

[14]  Lane Maria Rabelo Baccarini,et al.  The design of multiple linear regression models using a genetic algorithm to diagnose initial short-circuit faults in 3-phase induction motors , 2018, Appl. Soft Comput..

[15]  Haroldo de Faria,et al.  A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis , 2015 .

[16]  Jian Jiao,et al.  Forecasting of Dissolved Gases in Oil-immersed Transformers Based upon Wavelet LS-SVM Regression and PSO with Mutation , 2016 .

[17]  Mani Bhushan,et al.  Optimal Feature Selection for SVM Based Fault Diagnosis in Power Transformers , 2013 .

[18]  Makarand Sudhakar Ballal,et al.  Adaptive Neural Fuzzy Inference System for the Detection of Inter-Turn Insulation and Bearing Wear Faults in Induction Motor , 2007, IEEE Transactions on Industrial Electronics.

[19]  Yong Hu,et al.  The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature , 2011, Decis. Support Syst..

[20]  Andreas Hotho,et al.  MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data , 2016, Data Mining and Knowledge Discovery.

[21]  Bo Fan,et al.  Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis , 2014 .

[22]  Dong Ye,et al.  Robust locally linear embedding algorithm for machinery fault diagnosis , 2018, Neurocomputing.

[23]  Jianqiu Li,et al.  Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles , 2018 .

[24]  V. Sugumaran,et al.  Misfire detection in an IC engine using vibration signal and decision tree algorithms , 2014 .

[25]  Bhavesh R. Bhalja,et al.  Development of a new fault zone identification scheme for busbar using logistic regression classifier , 2017 .

[26]  Rene de Jesus Romero-Troncoso,et al.  Fractal dimension and fuzzy logic systems for broken rotor bar detection in induction motors at start-up and steady-state regimes , 2017 .

[27]  Shengrui Wang,et al.  Multiple Bayesian discriminant functions for high-dimensional massive data classification , 2016, Data Mining and Knowledge Discovery.

[28]  Amandeep S. Sidhu,et al.  A methodological review of data mining techniques in predictive medicine: An application in hemodynamic prediction for abdominal aortic aneurysm disease , 2014 .

[29]  Guoqiang Hu,et al.  Fault detection and diagnosis for building cooling system with a tree-structured learning method , 2016 .

[30]  Shen Yin,et al.  Recursive Total Principle Component Regression Based Fault Detection and Its Application to Vehicular Cyber-Physical Systems , 2018, IEEE Transactions on Industrial Informatics.

[31]  Shalabh Gupta,et al.  Fault diagnostics in smart micro-grids: A survey , 2016 .

[32]  Jian Pei,et al.  Classification with label noise: a Markov chain sampling framework , 2018, Data Mining and Knowledge Discovery.

[33]  Martin Valtierra-Rodriguez,et al.  Experimental data-based transient-stationary current model for inter-turn fault diagnostics in a transformer , 2017 .

[34]  Yiqi Liu,et al.  A review and comparison of fault detection and diagnosis methods for squirrel-cage induction motors: State of the art. , 2017, ISA transactions.

[35]  N. R. Sakthivel,et al.  Vibration based fault diagnosis of monoblock centrifugal pump using decision tree , 2010, Expert Syst. Appl..

[36]  Mo-Yuen Chow,et al.  A classification approach for power distribution systems fault cause identification , 2006, IEEE Transactions on Power Systems.

[37]  Sheng-wei Fei,et al.  Fault diagnosis of power transformer based on support vector machine with genetic algorithm , 2009, Expert Syst. Appl..

[38]  Hossam A. Gabbar,et al.  A new methodology for multiple incipient fault diagnosis in transmission lines using QTA and Naïve Bayes classifier , 2018, International Journal of Electrical Power & Energy Systems.

[39]  Rong-xing Duan,et al.  A New Fault Diagnosis Method Based on Fault Tree and Bayesian Networks , 2012 .

[40]  Raymond R. Tan,et al.  Improving the Reliability of Photovoltaic and Wind Power Storage Systems Using Least Squares Support Vector Machine Optimized by Improved Chicken Swarm Algorithm , 2019, Applied Sciences.

[41]  Szymon Jaroszewicz,et al.  Linear regression for uplift modeling , 2018, Data Mining and Knowledge Discovery.

[42]  Yu Qin,et al.  A Panel Data Model-Based Multi-Factor Predictive Model of Highway Electromechanical Equipment Faults , 2018, IEEE Transactions on Intelligent Transportation Systems.

[43]  Anamika Yadav,et al.  Complete protection scheme for fault detection, classification and location estimation in HVDC transmission lines using support vector machines , 2017 .

[44]  Hazlie Mokhlis,et al.  Fault location and detection techniques in power distribution systems with distributed generation: A review , 2017 .

[45]  Yekang Ko,et al.  Socio-technical evolution of Decentralized Energy Systems: A critical review and implications for urban planning and policy , 2016 .

[46]  Sanjeev Kumar Sharma,et al.  Fault identification in electrical power distribution system using combined discrete wavelet transform and fuzzy logic , 2015 .

[47]  David Camarena-Martinez,et al.  Fractal dimension-based approach for detection of multiple combined faults on induction motors , 2016 .

[48]  Emilio García Moreno,et al.  Fault Diagnosis of Electric Transmission Lines using Modular Neural Networks , 2016 .

[49]  C. Koley,et al.  Performance of a load-immune classifier for robust identification of minor faults in induction motor stator winding , 2014, IEEE Transactions on Dielectrics and Electrical Insulation.

[50]  Yong-kuo Liu,et al.  Support vector ensemble for incipient fault diagnosis in nuclear plant components , 2018, Nuclear Engineering and Technology.

[51]  Inmaculada Zamora,et al.  Plug-in electric vehicles in electric distribution networks: A review of smart charging approaches , 2014 .

[52]  Meiqin Liu,et al.  Data-Based Line Trip Fault Prediction in Power Systems Using LSTM Networks and SVM , 2018, IEEE Access.

[53]  Wen Shen,et al.  ARX model based fault detection and diagnosis for chillers using support vector machines , 2014 .

[54]  V. Sugumaran,et al.  Feature extraction using wavelets and classification through decision tree algorithm for fault diagnosis of mono-block centrifugal pump , 2013 .

[55]  Michael Wetter,et al.  Robust on-line fault detection diagnosis for HVAC components based on nonlinear state estimation techniques , 2014 .

[56]  Ming-Ta Yang,et al.  Intelligent fault types diagnostic system for dissolved gas analysis of oil-immersed power transformer , 2013, IEEE Transactions on Dielectrics and Electrical Insulation.

[57]  Bhim Singh,et al.  Incipient Interturn Fault Diagnosis in Induction Machines Using an Analytic Wavelet-Based Optimized Bayesian Inference , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[58]  Krishna R. Pattipati,et al.  Novel classifier fusion approahces for fault diagnosis in automotive systems , 2009, 2007 IEEE Autotestcon.

[59]  In-Soo Lee,et al.  Fault Diagnosis of Induction Motor Using Convolutional Neural Network , 2019, Applied Sciences.

[60]  Chee Peng Lim,et al.  Online Motor Fault Detection and Diagnosis Using a Hybrid FMM-CART Model , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[61]  V. Sugumaran,et al.  A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis , 2012, Appl. Soft Comput..

[62]  V. Sugumaran,et al.  Fault diagnosis of monoblock centrifugal pump using SVM , 2014 .

[63]  Subhransu Ranjan Samantaray,et al.  Decision Tree based discrimination between inrush currents and internal faults in power transformer , 2011 .

[64]  Li Xiu,et al.  Application of data mining techniques in customer relationship management: A literature review and classification , 2009, Expert Syst. Appl..

[65]  N. Ramesh Babu,et al.  Fault classification in power systems using EMD and SVM , 2017 .

[66]  C. Koley,et al.  Wavelet-aided SVM tool for impulse fault identification in transformers , 2006, IEEE Transactions on Power Delivery.

[67]  Zhi-Qiang Li,et al.  Least squares support vector machine for class imbalance learning and their applications to fault detection of aircraft engine , 2019, Aerospace Science and Technology.

[68]  Shih-Cheng Horng,et al.  A Classification-Based Fault Detection and Isolation Scheme for the Ion Implanter , 2006, IEEE Transactions on Semiconductor Manufacturing.

[69]  Purushottam Gangsar,et al.  Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms , 2017 .

[70]  Zahra Moravej,et al.  A hybrid method for arcing faults detection in large distribution networks , 2018 .

[71]  Bo Zhou,et al.  A Quantitative Study on the Void Defects Evolving into Damage in Wind Turbine Blade Based on Internal Energy Storage , 2020, Applied Sciences.

[72]  Enrico Zio,et al.  A fuzzy decision tree method for fault classification in the steam generator of a pressurized water reactor , 2009 .

[73]  Karen E. Frey,et al.  Mining and climate change: A review and framework for analysis , 2018 .

[74]  Liangyu Ma,et al.  An Intelligent Power Plant Fault Diagnostics for Varying Degree of Severity and Loading Conditions , 2010, IEEE Transactions on Energy Conversion.

[75]  Fabrizio Maria Maggi,et al.  Temporal stability in predictive process monitoring , 2018, Data Mining and Knowledge Discovery.

[76]  Sangho Ko,et al.  Application of fault factor method to fault detection and diagnosis for space shuttle main engine , 2016 .

[77]  Ahmed Patel,et al.  Design and evaluation of a hybrid system for detection and prediction of faults in electrical transformers , 2015 .

[78]  K X Lai,et al.  Application of data mining on partial discharge part I: predictive modelling classification , 2010, IEEE Transactions on Dielectrics and Electrical Insulation.

[79]  Jinquan Huang,et al.  Echo state kernel recursive least squares algorithm for machine condition prediction , 2018, Mechanical Systems and Signal Processing.

[80]  Martin Valtierra-Rodriguez,et al.  Shannon Entropy Index and a Fuzzy Logic System for the Assessment of Stator Winding Short-Circuit Faults in Induction Motors , 2019, Electronics.

[81]  Frédéric Magoulès,et al.  Development of an RDP neural network for building energy consumption fault detection and diagnosis , 2013 .

[82]  Chee Peng Lim,et al.  Fault Detection and Diagnosis of Induction Motors Using Motor Current Signature Analysis and a Hybrid FMM–CART Model , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[83]  S. Narendranath,et al.  Fault Diagnosis of Face Milling Tool using Decision Tree and Sound Signal , 2018 .

[84]  Roberto Saletti,et al.  Hybrid Micro-Grids Exploiting Renewables Sources, Battery Energy Storages, and Bi-Directional Converters , 2019 .

[85]  Khmais Bacha,et al.  Power transformer fault diagnosis based on dissolved gas analysis by support vector machine , 2012 .

[86]  Pierre Baldi,et al.  The inner and outer approaches to the design of recursive neural architectures , 2017, Data Mining and Knowledge Discovery.

[87]  Gerard Ledwich,et al.  A novel fuzzy logic approach to transformer fault diagnosis , 2000 .

[88]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[89]  Rene de Jesus Romero-Troncoso,et al.  Novel Downsampling Empirical Mode Decomposition Approach for Power Quality Analysis , 2016, IEEE Transactions on Industrial Electronics.

[90]  Zhou Yunlong,et al.  Vibration Fault Diagnosis Method of Centrifugal Pump Based on EMD Complexity Feature and Least Square Support Vector Machine , 2012 .

[91]  K. I. Ramachandran,et al.  Intelligent fault diagnosis of synchronous generators , 2016, Expert Syst. Appl..

[92]  Chi-Kin Chau,et al.  Personalized Prediction of Vehicle Energy Consumption Based on Participatory Sensing , 2016, IEEE Transactions on Intelligent Transportation Systems.

[93]  Torsten Bertram,et al.  Model-based remaining driving range prediction in electric vehicles by using particle filtering and Markov chains , 2013, 2013 World Electric Vehicle Symposium and Exhibition (EVS27).

[94]  N. R. Sakthivel,et al.  Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump , 2010 .

[95]  Haoran Zhao,et al.  Review of energy storage system for wind power integration support , 2015 .

[96]  Rene de Jesus Romero-Troncoso,et al.  FPGA-based entropy neural processor for online detection of multiple combined faults on induction motors , 2012 .

[97]  Leslie K. Norford,et al.  Robust model-based fault diagnosis for air handling units , 2015 .

[98]  Chao-Ming Huang,et al.  Fault Diagnosis of Power Transformers Using Computational Intelligence: A Review , 2012 .

[99]  Martin Valtierra-Rodriguez,et al.  A New Methodology for Tracking and Instantaneous Characterization of Voltage Variations , 2016, IEEE Transactions on Instrumentation and Measurement.

[100]  Martin Valtierra-Rodriguez,et al.  Fractal dimension and data mining for detection of short-circuited turns in transformers from vibration signals , 2019, Measurement Science and Technology.

[101]  Feng Lu,et al.  Aircraft engine degradation prognostics based on logistic regression and novel OS-ELM algorithm , 2019, Aerospace Science and Technology.

[102]  Lijun Yang,et al.  Particle swarm optimization-least squares support vector regression based forecasting model on dissolved gases in oil-filled power transformers , 2011 .

[103]  D. N. Vishwakarma,et al.  A Novel Methodology for Identifying Cross-Country Faults in Series-Compensated Double Circuit Transmission Lines , 2018 .

[104]  Yann-Chang Huang,et al.  Dissolved gas analysis of mineral oil for power transformer fault diagnosis using fuzzy logic , 2013, IEEE Transactions on Dielectrics and Electrical Insulation.

[105]  Borut Mavko,et al.  Application of the fault tree analysis for assessment of power system reliability , 2009, Reliab. Eng. Syst. Saf..

[106]  Enrico Zio,et al.  Fault Detection in Nuclear Power Plants Components by a Combination of Statistical Methods , 2013, IEEE Transactions on Reliability.

[107]  Wahidah Husain,et al.  Data Mining in Healthcare – A Review , 2015 .

[108]  Avagaddi Prasad,et al.  A review on fault classification methodologies in power transmission systems: Part-II , 2018 .

[109]  S. P. Ang,et al.  Causes of transformer failures and diagnostic methods – A review , 2018 .

[110]  Jiafu Wan,et al.  Industrial Big Data for Fault Diagnosis: Taxonomy, Review, and Applications , 2017, IEEE Access.

[111]  Samsul Bahari Mohd Noor,et al.  Broken Rotor Bar Fault Detection and Classification Using Wavelet Packet Signature Analysis Based on Fourier Transform and Multi-Layer Perceptron Neural Network , 2017 .

[112]  Wenyu Zhang,et al.  Kernel mixture model for probability density estimation in Bayesian classifiers , 2018, Data Mining and Knowledge Discovery.

[113]  Bo Zhang,et al.  Intelligent Fault Diagnosis Under Varying Working Conditions Based on Domain Adaptive Convolutional Neural Networks , 2018, IEEE Access.

[114]  Tahsin Koroglu,et al.  A comprehensive review on estimation strategies used in hybrid and battery electric vehicles , 2015 .

[115]  Guang Wang,et al.  Quality-Related Fault Detection and Diagnosis Based on Total Principal Component Regression Model , 2018, IEEE Access.

[116]  Fei Wang,et al.  Robust finite mixture regression for heterogeneous targets , 2018, Data Mining and Knowledge Discovery.

[117]  Bo-Suk Yang,et al.  Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference , 2009, Expert Syst. Appl..

[118]  Gerasimos Rigatos,et al.  Power transformers’ condition monitoring using neural modeling and the local statistical approach to fault diagnosis , 2016 .