Parameter Optimisation in the Vibration-Based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines
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[1] Ian K. Jennions,et al. Unbalance localization through machine nonlinearities using an artificial neural network approach , 2014 .
[2] Enrico Zio,et al. Artificial intelligence for fault diagnosis of rotating machinery: A review , 2018, Mechanical Systems and Signal Processing.
[3] Min Xia,et al. Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks , 2018, IEEE/ASME Transactions on Mechatronics.
[4] Changqing Shen,et al. Recognition of rolling bearing fault patterns and sizes based on two-layer support vector regression machines , 2014 .
[5] Jyoti K. Sinha,et al. Blind Application of Developed Smart Vibration-Based Machine Learning (SVML) Model for Machine Faults Diagnosis to Different Machine Conditions , 2020, Journal of Vibration Engineering & Technologies.
[6] Janani Shruti Rapur,et al. Experimental Time-Domain Vibration-Based Fault Diagnosis of Centrifugal Pumps Using Support Vector Machine , 2017 .
[7] Liang Guo,et al. A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines , 2018, Neurocomputing.
[8] Jiawei Xiang,et al. Rolling bearing fault diagnosis approach using probabilistic principal component analysis denoising and cyclic bispectrum , 2016 .
[9] Jyoti K. Sinha,et al. Unified Multi-speed Analysis (UMA) for the Condition Monitoring of Aero-Engines , 2015 .
[10] Nalinaksh S. Vyas,et al. Artificial neural network design for fault identification in a rotor-bearing system , 2001 .
[11] P. MacConnell,et al. Crack detection in a rotating shaft using artificial neural networks and PSD characterisation , 2014 .
[12] Wei Guo,et al. A hybrid intelligent multi-fault detection method for rotating machinery based on RSGWPT, KPCA and Twin SVM. , 2017, ISA transactions.
[13] Ran Zhang,et al. Transfer Learning With Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions , 2017, IEEE Access.
[14] Zhiqiang Chen,et al. Deep neural networks-based rolling bearing fault diagnosis , 2017, Microelectron. Reliab..
[15] Rajiv Tiwari,et al. Identification of suction flow blockages and casing cavitations in centrifugal pumps by optimal support vector machine techniques , 2017 .
[16] Yi Chai,et al. Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine , 2017 .
[17] A. K. Rigler,et al. Accelerating the convergence of the back-propagation method , 1988, Biological Cybernetics.
[18] Mohsen Esfahanian,et al. Fault identification in rotating machinery using artificial neural networks , 2005 .
[19] E. P. de Moura,et al. Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses , 2011 .
[20] Wu Deng,et al. Fault Diagnosis Method Based on Principal Component Analysis and Broad Learning System , 2019, IEEE Access.
[21] Peng Chen,et al. Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery , 2018, IEEE Transactions on Instrumentation and Measurement.
[22] Jyoti K. Sinha,et al. Development of a generic rotating machinery fault diagnosis approach insensitive to machine speed and support type , 2015 .