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Results for Fault detection

Publications & Outputs

  1. Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning

    Xia, M., Shao, H., Williams, D., Lu, S., Shu, L. & de Silva, C. W., 30/11/2021, In: Reliability Engineering and System Safety. 215, 9 p., 107938.

    Research output: Contribution to journalJournal articlepeer-review

  2. Stray Flux-Based Rotation Angle Measurement for Bearing Fault Diagnosis in Variable-Speed BLDC Motors

    Wang, X., Lu, S., Cao, W., Xia, M., Chen, K., Ding, J. & Zhang, S., 12/05/2021, In: IEEE Transactions on Energy Conversion.

    Research output: Contribution to journalJournal articlepeer-review

  3. Adversarial domain-invariant generalization: a generic domain-regressive framework for bearing fault diagnosis under unseen conditions

    Chen, L., Li, Q., Shen, C., Zhu, J., Wang, D. & Xia, M., 11/05/2021, In: IEEE Transactions on Industrial Informatics. 10 p.

    Research output: Contribution to journalJournal articlepeer-review

  4. 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 journalJournal articlepeer-review

  5. Multi-scale deep intra-class transfer learning for bearing fault diagnosis

    Wang, X., Shen, C., Xia, M., Wang, D., Zhu, J. & Zhu, Z., 1/10/2020, In: Reliability Engineering and System Safety. 202, 15 p., 107050.

    Research output: Contribution to journalJournal articlepeer-review

  6. Which Software Faults Are Tests Not Detecting?

    Petric, J., Hall, T. & Bowes, D., 15/04/2020, PROCEEDINGS of EASE 2020: Evaluation and Assessment in Software Engineering. New York: ACM, p. 160-169 10 p.

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

  7. 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 journalJournal articlepeer-review

  8. 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 journalJournal articlepeer-review

  9. Fully unsupervised fault detection and identification based on recursive density estimation and self-evolving cloud-based classifier

    Costa, B. S. J., Angelov, P. & Guedes, L. A., 20/02/2015, In: Neurocomputing. 150, A, p. 289-303 15 p.

    Research output: Contribution to journalJournal articlepeer-review

  10. RDE with forgetting: an approximate solution for large values of k with an application to fault detection problems

    Bezerra, C. G., Costa, B., Guedes, L. A. & Angelov, P., 2015, Statistical learning and data sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. Gammerman, A., Vovk, V. & Papadopoulos, H. (eds.). Springer, p. 169-178 10 p. (Lecture Notes in Computer Science; vol. 9047).

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

  11. Nonlinear system identification for model-based condition monitoring of wind turbines

    Cross, P. & Ma, X., 11/2014, In: Renewable Energy. 71, p. 166-175 10 p.

    Research output: Contribution to journalJournal articlepeer-review

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