[1] L.Z. Zepeng Liu, A Review of Failure modes, Condition Monitoring and Fault Diagnosis Methods for Large-Scale Wind Turbine Bearings, Measurement, 149 (2020).
[2] K.-L.T. Dong Wang, Qiang Miao Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators, IEEE Access, 6 (2017) 665 - 676.
[3] B.S. Gandhare, Maintenance Strategy Selection, in: Ninth AIMS International Conference on Management, 2012.
[4] R.K. Mobley, An introduction to predictive maintenance Butterworth-Heinemann, America, 2002.
[5] B.S. Gandhare, Maintenance Strategy Selection, presented at the Ninth AIMS International Conference on Management, (2012).
[6] B.A. Gandhare, Milind, Maintenance Strategy Selection, in: The 9th AIMS International Conference on Management, 2012.
[7] N.L. Liang Guo, Feng Jia, Yaguo Lei, Jing Lin, A Recurrent Neural Network Based Health Indicator for Remaining Useful Life Prediction of Bearings, Neurocomputing, 240 (2017) 98–109.
[8] S.H.U. Akhand Rai, A Aeview on Signal Processing Techniques Utilized in the Fault Diagnosis of Rolling Element Bearings, Tribol. Int., 96 (2016) 289-306.
[9] K.-C.L.G.G. Yen, Wavelet Packet Feature Extraction for Vibration Monitoring, IEEE Transactions on Industrial Electronics, 47(3) (2000) 650 - 667.
[10] M.S.H. J. L. Won Gi Lee, Sung-Ho Nam, YongHo Jeon, andMoon G. Lee, Failure Diagnosis System for a Ball-Screw by Using Vibration Signals, Hindawi Publishing Corporation Shock and Vibration, (2015).
[11] G.P.C. Christopher Torrence, A Practical Guide to Wavelet Analysis, American Meteorological Society, 79(1) (1998) 61-78.
[12] Y.H. C. C. P. Tsai, Ball Screw Preload Loss Detection Using Ball Pass Frequency, Mechanical Systems and Signal Processing, 48 (2014) 77-91.
[13] J.L. Y. L. Feng Jia, Xin Zhou, and Na Lu, Deep Neural Networks: A Promising Tool for Fault Characteristic Mining and Intelligent Diagnosis of Rotating Machinery with massive Data, Mechanical Systems and Signal Processing, 72 (2016) 303-315.
[14] W.-L.Q. Z.-Y. W. Chen Lu, Jian Ma, Fault Diagnosis of Rotary Machinery Components Using a Stacked Denoising Autoencoder-Based Health State Identification, Signal Processing, 130 (2017) 377–388.
[15] J.-G.B. Youngji Yoo, A Novel Image Feature for the Remaining Useful Lifetime Prediction of Bearings Based on Continuous Wavelet Transform and Convolutional Neural Network, Appl. Sci., 8(7) (2018).
[16] S.S. Wathiq Abed, Robert Sutton, Amit Motwani A Robust Bearing Fault Detection and Diagnosis Technique for Brushless DC Motors Under Non-stationary Operating Conditions, J. Control. Autom. Electr. Syst., 26 (2015) 241–254.
[17] M.O. Jun He, Chen Yong, Danfeng Chen, Jing Guo, Yan Zhou, A Novel Intelligent Fault Diagnosis Method for Rolling Bearing Based on Integrated Weight Strategy Features Learning, sensors, 20(6) (2020).
[18] Z.L. Hang Yin, Jiankai Zuo, Hedan Liu, Kang Yang, Fei Li, Wasserstein Generative Adversarial Network and Convolutional Neural Network (WG-CNN) for Bearing Fault Diagnosis, Math. Probl. Eng., 2020 (2020).
[19] S.Z. Shen Zhang, Bingnan Wang, Thomas G. Habetler, Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review, IEEE Access 8(2020) 29857 - 29881.
[20] T.Y. Samir Khan, A Review on the Application of Deep Learning in System Health Management, Mech. Syst. Signal Process, 107 (2018) 241-265.
[21] J.P.a.H.W.v.d.V. Mohammadreza Kaji, Constructing a Reliable Health Indicator for Bearings Using Convolutional Autoencoder and Continuous Wavelet Transform, Appl. Sci., (2020).
[22] T.G.H. Wei Zhou, Ronald G. Harley, Bearing Condition Monitoring Methods for Electric Machines: A General Review, in: 2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, Cracow, Poland, 2007.
[23] S.V.K. Prashant P. Kharche, Review of Fault Detection in Rolling Element Bearing, International Journal of Innovative Research in Advanced Engineering, 1(5) (2014) 169-174.
[24] S.C.S. P.K. Kankar, S.P. Harsha, Fault Diagnosis of Ball Bearings Using Continuous Wavelet Transform, Appl. Soft Comput., 11 (2011) 2300–2312.
[25] PCoE Datasets, Bearing Data Set, Intelligent Maintenance Systems (IMS), University of Cincinnati, in.
[26] A.B. Andrea Borghesi, Luca Benini, Anomaly Detection using Autoencoders in High Performance Computing Systems, in: The Thirty-First AAAI Conference on Innovative Applications of Artificial Intelligence, Hawaii, USA, 2019.
[27] G.G. Fangyi Wan, Chunlin Zhang, Qing Guo, Jie Liu, Outlier Detection for Monitoring Data Using Stacked Autoencoder, IEEE Access 7(2019) 173827 - 173837.
[28] M.S.L. Ngui Wai Keng, Lim Meng Hee, Ahmed. M. Abdelrhman, Wavelet Analysis: Mother Wavelet Selection Methods, Applied Mechanics and Materials, 393 (2013) 953-958.
[29] K.-C.L. G.G. Yen, Wavelet packet feature extraction for vibration monitoring, IEEE Transactions on Industrial Electronics 47(3) (2000) 650 - 667.
[30] H.Y. Yasi Wang, Sicheng Zhao, Auto-Encoder Based Dimensionality Reduction, Neurocomputing, 184 (2015) 232-242.
[31] F. Chollet, Keras: Deep Learning Library for Theano and Tensorflow.
[32] H.T. Ahmed Ali Mohammed Al-Saffar, Mohammed Ahmed Talab, Review of Deep Convolution Neural Network in Image Classification, in: International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications, IEEE, Jakarta, Indonesia 2018.
[33] A.M. Saleh Albelwi, A Framework for Designing the Architectures of Deep Convolutional Neural Networks, Entropy, 19(6) (2017).
[34] M.H.L. Jeongyoun Ahn, Jung Ae Lee, Distance-Based Outlier Detection for High Dimension, Low Sample Size Data, J. Appl. Stat., 46(1) (2019) 13-29.
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