Gas Turbine Rotor Fault Diagnosis Based on Domain Adversarial Adaptation Transfer Learning for Small Samples
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-69483-7_32
Reference12 articles.
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2. De Giorgi, M.G., Campilongo, S., Ficarella, A.: A diagnostics tool for aero-engines health monitoring using machine learning technique. Energy Procedia 148, 860–867 (2018)
3. Mousavi, M., Mm, H., Chaibakhsh, A.: Ensemble-based fault detection and isolation of an industrial gas turbine. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2351–2358. IEEE, Toronto, Canada (2020)
4. Chen, M., Hu, L.Q., Tang, H.: An approach for optimal measurements selection on gas turbine engine fault diagnosis. J. Eng. Gas Turbines Power 137(7), 071–203 (2015)
5. Shi-sheng, Z., et al.: A novel gas turbine fault diagnosis method based on transfer learning with CNN. Measurement 137, 435–453 (2019)
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