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1. Full Text Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network
by Bin, G.F and Gao, J.J and Li, X.J and Dhillon, B.S
Mechanical systems and signal processing, ISSN 0888-3270, 2012, Volume 27, Issue 1, pp. 696 - 711
Early fault diagnosis | Feature extraction | WPD | Energy moment | Neural network | EMD | Engineering | Technology | Engineering, Mechanical | Science & Technology | Magneto-electric machines | Neural networks | Machinery | Mechanical engineering | Analysis | Fault diagnosis | Faults | Rotating machinery | Rotors | Bandwidth | Decomposition
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2. Design of rotating electrical machines
by Pyrhönen, Juha and Jokinen, Tapani, 1937 and Hrabovcová, Valeria and Books24x7, Inc
2008, 1. Aufl., ISBN 9780470695166, xxv, 512
Design and construction | Electric machinery | Rotational motion | Electric generators | Electric motors
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3. Full Text Artificial intelligence for fault diagnosis of rotating machinery: A review
by Liu, Ruonan and Yang, Boyuan and Zio, Enrico and Chen, Xuefeng
Mechanical systems and signal processing, ISSN 0888-3270, 08/2018, Volume 108, pp. 33 - 47
Fault diagnosis | Deep learning | Naive Bayes | Rotating machinery | Support vector machine | Artificial neural network | Artificial intelligence | k-Nearest neighbour | Engineering | Technology | Engineering, Mechanical | Science & Technology | Algorithms | Mechanical engineering | Industrial equipment | Studies | Support vector machines | Artificial neural networks | Machine learning | Industrial applications | Bayesian analysis | Rotation | System effectiveness | Statistics
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4. Full Text Time–frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis
by Wang, Lei and Liu, Zhiwen and Miao, Qiang and Zhang, Xin
Mechanical systems and signal processing, ISSN 0888-3270, 03/2018, Volume 103, pp. 60 - 75
Ensemble local mean decomposition | Fault diagnosis | Rotating machinery | Fast kurtogram | Engineering | Technology | Engineering, Mechanical | Science & Technology | Signal processing | Noise pollution | Magneto-electric machines | Machinery | Analysis | Studies | Signaling | Time-frequency analysis | Kurtosis | Bandpass filters | Decomposition | Background noise
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5. Condition monitoring of rotating electrical machines
by Tavner, Peter J., 1946 and Books24x7, Inc
2008, IET power and energy series, ISBN 0863417418, Volume 56, xxii, 282
Electric machinery | Monitoring | Electric machinery -- Monitoring | Electric machinery--Monitoring | Electrical engineering | Rotational motion | Electric generators | Electric motors
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6. Full Text A review on empirical mode decomposition in fault diagnosis of rotating machinery
by Lei, Yaguo and Lin, Jing and He, Zhengjia and Zuo, Ming J
Mechanical systems and signal processing, ISSN 0888-3270, 02/2013, Volume 35, Issue 1-2, pp. 108 - 126
Fault diagnosis | Empirical mode decomposition | Intrinsic mode function | Rotating machinery | Engineering | Technology | Engineering, Mechanical | Science & Technology | Usage | Magneto-electric machines | Machinery | Mechanical engineering | Analysis | Faults | Conferences | Bearings | Signal processing | Decomposition | Empirical analysis
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7. Full Text A novel deep autoencoder feature learning method for rotating machinery fault diagnosis
by Shao, Haidong and Jiang, Hongkai and Zhao, Huiwei and Wang, Fuan
Mechanical systems and signal processing, ISSN 0888-3270, 10/2017, Volume 95, pp. 187 - 204
Fault diagnosis | Feature learning | Maximum correntropy | Artificial fish swarm algorithm | Deep autoencoder | Engineering | Technology | Engineering, Mechanical | Science & Technology | Machinery | Methods | Magneto-electric machines | Algorithms | Locomotives | Analysis | Electric vehicles | Signaling | Vibration | Rotating machinery | Machine learning | Vibration measurement | Maximum entropy method
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8. Full Text Metamodeling of Rotating Electric Machinery
by Sudhoff, Scott D and Sahu, Raj
IEEE transactions on energy conversion, ISSN 0885-8969, 09/2018, Volume 33, Issue 3, pp. 1058 - 1071
Torque | design optimization | Metamodeling | Rotors | Stators | system design | Conductors | Electric machinery | losses | Power generation | metamodeling | Engineering, Electrical & Electronic | Engineering | Technology | Energy & Fuels | Science & Technology | Permanent magnets | Rotating machinery | Multiple objective | Tradeoffs | Scaling laws | Design optimization
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9. Full Text Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data
by Jia, Feng and Lei, Yaguo and Lin, Jing and Zhou, Xin and Lu, Na
Mechanical systems and signal processing, ISSN 0888-3270, 05/2016, Volume 72-73, pp. 303 - 315
Deep learning | Deep neural networks | Intelligent fault diagnosis | Rotating machinery | Massive data | Engineering | Technology | Engineering, Mechanical | Science & Technology | Signal processing | Mineral industry | Magneto-electric machines | Neural networks | Machinery | Mining industry | Artificial intelligence | Fault diagnosis | Architecture | Nonlinearity | Feature extraction | Diagnosis
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