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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
Journal Article
2008, 1. Aufl., ISBN 9780470695166, xxv, 512
Book
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
Journal Article
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
Journal Article
2008, IET power and energy series, ISBN 0863417418, Volume 56, xxii, 282
Book
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
Journal Article
7.
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A novel deep autoencoder feature learning method for rotating machinery fault diagnosis
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
Journal Article
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
Journal Article
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
Journal Article