Open Access
Issue
MATEC Web Conf.
Volume 255, 2019
Engineering Application of Artificial Intelligence Conference 2018 (EAAIC 2018)
Article Number 06008
Number of page(s) 10
Section Health Monitoring and Diagnosis
DOI https://doi.org/10.1051/matecconf/201925506008
Published online 16 January 2019
  1. T.A. Piedras Lopes, A.C.R. Troyman, Neural networks on predictive maintenance of turbomachinery, IFAC Fault Detection, Supervision and Safety for Technical Processess, Kingston Upon Hull, UK (1997) [Google Scholar]
  2. P. Jahnke, Machine learning approaches for failure type detection and predictive maintenance, Thesis, Dept. of Computer Science, TU Darmstadt (2015) [Google Scholar]
  3. L. Martí, N. Sanchez-Pi, J.M. Molina, A.C.B. Garcia, Anomaly detection based on sensor data in petroleum industry applications, Sensors 15, 2774–2797 (2015) [CrossRef] [Google Scholar]
  4. A. Blinder, W. Wojcikiewicz, C. Müller, M. Kawanabe, A hybrid supervised-unsupervised vocabulary generation algorithm for visual concept recognition, ACCV: Computer Vision, 95–108 (2010) [Google Scholar]
  5. K. Feng, Y. Wang, Y. Zhao, C.W. Fu, Z. Cheng, B. Chen, Cluster aware star coordinates, Journal of Visual Languages and Computing 44, 28–38 (2018) [CrossRef] [Google Scholar]
  6. M.T. AL-Sharuee, F. Liu, M. Pratama, Sentiment analysis: an automatic contextual analysis and ensemble clustering approach and comparison, Data & Knowledge Engineering 115, 194–213 (2018) [CrossRef] [Google Scholar]
  7. S.B. Salem, S. Naouali, Z. Chtourou, A fast and effective partitional clustering algorithm for large categorical datasets using a k-means based approach, Computers and electrical engineering 68, 463–483 (2018) [CrossRef] [Google Scholar]
  8. J.K. Yoo, Partial least squares fusing unsupervised learning, Chemometrics and Intelligent Laboratory Systems 175, 82–86 (2018) [CrossRef] [Google Scholar]
  9. R. Garcia-Dlas, C.A. Prieto, J.S. Almeida, I. Ordovás-Pascual, Machine learning in APOGEE: unsupervised spectral classification with k- means, Astronomy and Astrophysics 612, A98 (2018) [CrossRef] [Google Scholar]
  10. M.-J. Cho, R.R. Hallac, M. Effendi, J.R. Seaward, A.A. Kane, Comparison of an unsupervised machine learning algorithm and surgeon diagnosis in the clinical differentiation of metopic craniosynostosis and benign metopic ridge, Scientific Reports 8(1), 6312 (2018) [CrossRef] [Google Scholar]
  11. D. Reynolds, Gaussian Mixture Models, Encyclopedia of Biometrics (2009) [Google Scholar]
  12. J. Li, A. Nehorai, Gaussian mixture learning via adaptive hierarchical clustering, Signal Processing 120, 116–121 (2018) [CrossRef] [Google Scholar]
  13. R. Mohammadi-Ghazi, Y.M. Marzouk, O. Büyüköztürk, Conditional classifiers and boosted conditional Gaussian mixture model for novelty detection, Pattern Recognition 81, 601–614 (2018) [CrossRef] [Google Scholar]
  14. M.C. Madhavi, H.A. Patil, Design of mixture of GMMs for Query-by-Example spoken term detection, Computer Speech and Language 52, 41–55 (2018) [CrossRef] [Google Scholar]
  15. C.E. Bencheriet, New face feature to detect multiple faces in complex background, Evolving Systems, 1–17 (2017) [Google Scholar]
  16. T. Kawabata, Gaussian-input Gaussian mixture model for representing density maps and atomic models, Journal of Structural Biology 203(1), 1–16 (2018) [CrossRef] [Google Scholar]
  17. J. Navarrete, D. Viejo, M. Cazorla, Compression and registration of 3D point clouds using GMMs, Pattern Recognition Letters 110, 8–15 (2018) [CrossRef] [Google Scholar]
  18. J. Qu, C.V. Deutsch, Geostatistical simulation with a trend using Gaussian mixture models, Natural Resources Research 27(3), 347–363 (2018) [CrossRef] [Google Scholar]
  19. Y. Zhang, C. Bingham, M. Garlick, M. Gallimore, Applied fault detection and diagnosis for industrial gas turbine systems, International Journal of Automation and Computing 14(4), 463–473 (2017) [CrossRef] [Google Scholar]
  20. H.C. Pusey, Turbomachinery condition monitoring and failure prognosis, Sound and Vibration, (2007) [Google Scholar]
  21. S. Yang, X. Jiang, S. Xu, X. Wang, Bayesian stochastic neural network model for turbomachinery damage prediction, International Journal of Prognosis and Health Management 9(1), 018 (2018) [Google Scholar]

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