Results for Condition monitoring

Publications & Outputs

1. A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines

   Wu, Y. & Ma, X., 31/01/2022, In: Renewable Energy. 181, p. 554-566 13 p.

   Research output: Contribution to Journal/Magazine › Journal article › peer-review

2. Application of Spectral Kurtosis on vibration signals for the detection of cavitation in centrifugal pumps

   Mousmoulis, G., Yiakopoulos, C., Aggidis, G., Antoniadis, I. & Anagnostopoulos, I., 30/11/2021, In: Applied Acoustics. 182, 17 p., 108289 .

   Research output: Contribution to Journal/Magazine › Journal article › peer-review

3. Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

   Berghout, T., Benbouzid, M., Bentrcia, T., Ma, X., Djurović, S. & Mouss, L-H., 3/10/2021, In: Energies. 14, 19, 25 p., 6316.

   Research output: Contribution to Journal/Magazine › Literature review › peer-review

4. Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

   Benbouzid, M., Berghout, T., Sarma, N., Djurović, S., Wu, Y. & Ma, X., 20/09/2021, In: Energies. 14, 18, 33 p., 5967.

   Research output: Contribution to Journal/Magazine › Literature review › peer-review

5. Efficient Data Reduction at the Edge of Industrial Internet of Things for PMSM Bearing Fault Diagnosis

   Wang, X., Lu, S., Huang, W., Wanga, Q., Zhang, S. & Xia, M., 1/03/2021, In: IEEE Transactions on Instrumentation and Measurement. 70, 12 p., 3508612.

   Research output: Contribution to Journal/Magazine › Journal article › peer-review

6. Enhanced feature extraction method for motor fault diagnosis using low-quality vibration data from wireless sensor networks

   Shu, Q., Lu, S., Xia, M., Ding, J., Niu, J. & Liu, Y., 15/01/2020, In: Measurement Science and Technology. 31, 4, 17 p., 045016.

   Research output: Contribution to Journal/Magazine › Journal article › peer-review

7. Data-driven condition monitoring approaches to improving power output of wind turbines

   Qian, P., Ma, X., Zhang, D. & Wang, J., 1/08/2019, In: IEEE Transactions on Industrial Electronics. 66, 8, p. 6012 - 6020 9 p.

   Research output: Contribution to Journal/Magazine › Journal article › peer-review

8. Kullback-Leibler divergence based wind turbine fault feature extraction

   Wu, Y. & Ma, X., 1/07/2019, 24th International Conference on Automation & Computing. IEEE, p. 507-512 6 p.

   Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

9. Wind turbine fault detection and identification through PCA-based optimal variable selection

   Wang, Y., Ma, X. & Qian, P., 10/2018, In: IEEE Transactions on Sustainable Energy. 9, 4, p. 1627-1635 9 p.

   Research output: Contribution to Journal/Magazine › Journal article › peer-review

10. Reducing sensor complexity for monitoring wind turbine performance using principal component analysis

    Wang, Y., Ma, X. & Joyce, M. J., 11/2016, In: Renewable Energy. 97, p. 444–456 13 p.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

11. Simultaneous fault detection and sensor selection for condition monitoring of wind turbines

    Zhang, W. & Ma, X., 12/04/2016, In: Energies. 9, 4, 15 p., 280.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

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