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ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES
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Research works printed but not linked: 15
- MCCORMICK, 1997, Classification of the rotating machine condition using artificial neural networks, Proceedings of Institution of Mechanical Engineers, Part C, 211, 439, 10.1243/0954406971521845 · DOI
- MCCORMICK, 1997, Real-time classification of the rotating shaft loading conditions using artificial neural networks, IEEE Transactions on Neural Networks, 8, 748, 10.1109/72.572110 · DOI
- MCCORMICK, 1998, Application of periodic time-varying autoregressive models to the detection of bearing faults, Proceedings of Institution of Mechanical Engineers, Part C, 212, 417, 10.1243/0954406981521330 · doi:10.1243/0954406981521330
- ALGUINDIGUE, 1993, Monitoring and diagnosis of rolling element bearings using artificial neural networks, IEEE Transactions on Industrial Electronics, 40, 209, 10.1109/41.222642 · DOI
- PAYA, 1997, Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor, Mechanical Systems and Signal Processing, 11, 751, 10.1006/mssp.1997.0090 · DOI · Paper page
- CHOW, 1991, A neural network approach to real-time condition monitoring of induction motors, IEEE Transactions on Industrial Electronics, 38, 448, 10.1109/41.107100 · DOI
- SHIROISHI, 1997, Bearing condition diagnostics via vibration and acoustics emission measurements, Mechanical Systems and Signal Processing, 11, 693, 10.1006/mssp.1997.0113 · DOI · Paper page
- BARKOV, 1995, Condition assessment and life prediction of rolling element bearings, Sound and Vibration, 28, 10
- AZOVTSEV, 1996, Improving accuracy of rolling element bearing condition assessment, Proceedings of the 20th Annual Meeting of the Vibration Institute, 27
- LIU, 1994, Intelligent classification and measurement of drill wear, Transactions ASME, Journal of Engineering for Industry, 116, 392, 10.1115/1.2901957 · doi:10.1115/1.2901957
- LIU, 1998, Intelligent detection of drill wear, Mechanical Systems and Signal Processing, 12, 863, 10.1006/mssp.1998.0165 · DOI
- MAKI, 1997, A neural network approach to fault detection and diagnosis in industrial processes, IEEE Transactions on Control Systems Technology, 5, 529, 10.1109/87.641399 · doi:10.1109/87.641399
- MARTIN, 1999, Diagnostics of a coolant system via neural networks, Proceedings of Institution of Mechanical Engineers, Part I, 213, 229, 10.1243/0959651991540106 · doi:10.1243/0959651991540106
- BAILLIE, 1994, Nonlinear model-based fault diagnosis of bearings, Proceedings of International Conference on Condition Monitoring, University of Wales, 241
- HO, 2000, Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals, Mechanical Systems and Signal Processing, 14, 763, 10.1006/mssp.2000.1304 · DOI · Paper page
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Metadata
- Authors
- SAMANTA, B., & AL-BALUSHI, K. R.
- Venue
- Mechanical Systems and Signal Processing
- Publisher
- Elsevier BV
- Published
- 2003-03
- Volume
- 17
- Issue
- 2
- Pages
- 317-328
- ISSN
- 0888-3270 (ISSN-L)
- OpenAlex
- W2002616799
- Cited by
- 760
- References
- 0
- FWCI
- 24.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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- CoChat. (2026). Citation check: ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES. https://app.cochat.ai/papers/10.1006/mssp.2001.1462
- The paper
- SAMANTA, B., & AL-BALUSHI, K. R. (2003). ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES. Mechanical Systems and Signal Processing. https://doi.org/10.1006/mssp.2001.1462
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
