A cloud-based condition monitoring system for fault detection in rotating machines using PROFINET process data

[1]  Heiga Zen,et al.  Deep Learning for Acoustic Modeling in Parametric Speech Generation: A systematic review of existing techniques and future trends , 2015, IEEE Signal Processing Magazine.

[2]  Hongxun Yao,et al.  Auto-encoder based dimensionality reduction , 2016, Neurocomputing.

[3]  Guang Li,et al.  Analysis of PROFINET IO Communication Protocol , 2014, 2014 Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control.

[4]  Susana Ferreiro,et al.  Dynamic condition monitoring method based on dimensionality reduction techniques for data-limited industrial environments , 2019, Comput. Ind..

[5]  Daniel U. Campos-Delgado,et al.  Data fusion for multiple mechanical fault diagnosis in induction motors at variable operating conditions , 2010, 2010 7th International Conference on Electrical Engineering Computing Science and Automatic Control.

[6]  Rajiv Tiwari,et al.  Monitoring of Induction Motor Mechanical and Electrical Faults by Optimum Multiclass-Support Vector Machine Algorithms Using Genetic Algorithm , 2018 .

[7]  Sameh M. Metwalley,et al.  Condition Based Maintenance Optimization for Faulty Gearbox under Continuous Noise Monitoring , 2013 .

[8]  G. Munz,et al.  Distributed Network Analysis Using TOPAS and Wireshark , 2008, NOMS Workshops 2008 - IEEE Network Operations and Management Symposium Workshops.

[9]  Wang Dong-feng,et al.  Research on Early Fault Diagnostic Method of Wind Turbines , 2013 .

[10]  Rodrigo Nicoletti,et al.  Detection of cracks in shafts with the Approximated Entropy algorithm , 2016 .

[11]  Zaqiatud Darojah,et al.  Artificial Neural Network based identification system for abnormal vibration of motor rotating disc system , 2015, 2015 International Electronics Symposium (IES).

[12]  Adedeji B. Badiru,et al.  Online Support Vector Regression Approach for the Monitoring of Motor Shaft Misalignment and Feedwater Flow Rate , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[13]  Sudarson Jena,et al.  Correlation based feature selection with clustering for high dimensional data , 2018, Journal of Electrical Systems and Information Technology.

[14]  Guilherme Serpa Sestito,et al.  Performance Analysis of Profibus DP and Profinet in a Motion Control Application , 2017 .

[15]  Jose Mathew,et al.  Experimental Studies Using Response Surface Methodology for Condition Monitoring of Ball Bearings , 2010 .

[16]  Leandros A. Maglaras,et al.  Effect of Network Architecture Changes on OCSVM Based Intrusion Detection System , 2016, INISCOM.

[17]  Jasper Snoek,et al.  Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.

[18]  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 .

[19]  Alessandra Caggiano,et al.  Cloud-based manufacturing process monitoring for smart diagnosis services , 2018, Int. J. Comput. Integr. Manuf..

[20]  Kwok-Leung Tsui,et al.  Gear crack level classification based on multinomial logit model and cumulative link model , 2012, Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing).

[21]  Xuefeng Yan,et al.  Multiscale intelligent fault detection system based on agglomerative hierarchical clustering using stacked denoising autoencoder with temporal information , 2020, Appl. Soft Comput..

[22]  Antoine Picot,et al.  Current-Based Detection of Mechanical Unbalance in an Induction Machine Using Spectral Kurtosis With Reference , 2015, IEEE Transactions on Industrial Electronics.

[23]  Ayyaz Hussain,et al.  Classification of unbalance and misalignment faults in rotor using multi-axis time domain features , 2016, 2016 International Conference on Emerging Technologies (ICET).

[24]  Yurong Liu,et al.  A survey of deep neural network architectures and their applications , 2017, Neurocomputing.

[25]  Sufi Tabassum Gul,et al.  A comparative analysis of classical and one class SVM classifiers for machine fault detection using vibration signals , 2016, 2016 International Conference on Emerging Technologies (ICET).

[26]  Abhineet Saini,et al.  Intelligent predictive maintenance of dynamic systems using condition monitoring and signal processing techniques — A review , 2016, 2016 International Conference on Advances in Computing, Communication, & Automation (ICACCA) (Spring).

[27]  Yukio Mizuno,et al.  Development of a methodology for bearing fault scrutiny and diagnosis using SVM , 2017, 2017 IEEE International Conference on Industrial Technology (ICIT).

[28]  Ke Wang,et al.  The Application of AE Signal in Early Cracked Rotor Fault Diagnosis with PWVD and SVM , 2011, J. Softw..

[29]  Dennis Brandão,et al.  Panorama, challenges and opportunities in PROFINET protocol research , 2018, 2018 13th IEEE International Conference on Industry Applications (INDUSCON).

[30]  Yaguo Lei,et al.  Applications of machine learning to machine fault diagnosis: A review and roadmap , 2020 .

[31]  Jan Desmet,et al.  Misalignment and unbalance fault severity estimation using stator current measurements , 2017, 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED).

[32]  Giansalvo Cirrincione,et al.  Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks , 2013, IEEE Transactions on Industrial Electronics.

[33]  Emiliano Sisinni,et al.  Introducing a cloud based architecture for the distributed analysis of Real-Time Ethernet traffic , 2020, 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT.

[34]  Wlodzislaw Duch,et al.  Feature Selection for High-Dimensional Data - A Pearson Redundancy Based Filter , 2008, Computer Recognition Systems 2.

[35]  Noureddine Zerhouni,et al.  Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression , 2015, IEEE Transactions on Instrumentation and Measurement.

[36]  Enrico Zio,et al.  Artificial intelligence for fault diagnosis of rotating machinery: A review , 2018, Mechanical Systems and Signal Processing.

[37]  Ivan Nunes da Silva,et al.  Feature Extraction and Power Quality Disturbances Classification Using Smart Meters Signals , 2016, IEEE Transactions on Industrial Informatics.

[38]  Guigang Zhang,et al.  A fault diagnosis method of engine rotor based on Random Forests , 2016, 2016 IEEE International Conference on Prognostics and Health Management (ICPHM).

[39]  Paolo Ferrari,et al.  A Method for Anomalies Detection in Real-Time Ethernet Data Traffic Applied to PROFINET , 2018, IEEE Transactions on Industrial Informatics.