Publications

Full list on Google Scholar

Selected Journal Papers

  1. X. Xing, T.Yan, and M. Xia, "Early Prediction of Battery Life using an Interpretable Health Indicator with Evolutionary Computing," in Reliability Engineering & System Safety, vol. 260, no.110980, 2025.
  2. T. Yan, X. Xing, D. Wang, K. Tsui, and M. Xia," Interpretable Degradation Tensor Modeling Through Multi-scale and Multi-level Time-Frequency Feature Fusion for Machine Health Monitoring," in Information Fusion, vol. 117, no. 102935, May 2025.
  3. Liao, D. Wang, X. Ming and M. Xia, "DLCNN: A Deep Logic Convolutional Network for Interpretable Fault Diagnosis of Hoist Mechanism on Ship-to-Shore Cranes," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2025.3594346.
  4. F. Jiang, X. Hou, and M. Xia. "Spatio-temporal Attention-based Hidden Physics-informed Neural Network for Remaining Useful Life Prediction." in Advanced Engineering Informatics, vol. 63, no.102958, Jan. 2025.
  5. D. Zhao, J. Chen, H. Yin, L. Cai, and M. Xia, "A Novel Semi-Supervised Fault Diagnosis Method for Unbalanced Data," in IEEE Internet of Things Journal, vol. 12, no. 6, pp. 7599-7609, 2025.
  6. Wu and M.Xia, “A dual-objective contrastive learning approach with dynamic self-adaption for zero-shot fault diagnosis,” in Engineering Applications of Artificial Intelligence (accepted)
  7. Zhou, W. Chen, L. Cheng, J. Liu, and M. Xia, “Trustworthy Fault Diagnosis with Uncertainty Estimation through Evidential Convolutional Neural Networks,” IEEE Trans Industr Inform, 2023.
  8. Chen, H. Zhou, L. Cheng, J. Liu, and M. Xia, “Condition monitoring of wind turbine using novel deep learning method and dynamic kernel principal components Mahalanobis distance,” Eng Appl Artif Intell, vol. 125, p. 106757, 2023.
  9. Chen, H. Zhou, L. Cheng, and M. Xia, “Prediction of regional wind power generation using a multi-objective optimized deep learning model with temporal pattern attention,” Energy, p. 127942, 2023.
  10. Jiang, M. Xia, and Y. Hu, “Physics-Informed Machine Learning for Accurate Prediction of Temperature and Melt Pool Dimension in Metal Additive Manufacturing,” 3D Print Addit Manuf, 2023.
  11. Zhou, W. Chen, L. Cheng, D. Williams, C. W. De Silva, and M. Xia, “Reliable and Intelligent Fault Diagnosis With Evidential VGG Neural Networks,” IEEE Trans Instrum Meas, vol. 72, pp. 1–12, 2023.
  12. Zhu, Y. Wang, M. Xia, D. Williams, and C. W. De Silva, “A new multisensor partial domain adaptation method for machinery fault diagnosis under different working conditions,” IEEE Trans Instrum Meas, vol. 72, pp. 1-10, 2023, Art no. 3531410. (IEEE TIM Andy Chi Best Paper Award)
  13. Chen, K. An, D. Huang, X. Wang, M. Xia, and S. Lu, “Noise-Boosted Convolutional Neural Network for Edge-based Motor Fault Diagnosis with Limited Samples,” IEEE Trans Industr Inform, 2022.
  14. Ding, X. Hou, M. Xia, Y. Ismail, and J. Ye, “Predictions of macroscopic mechanical properties and microscopic cracks of unidirectional fibre-reinforced polymer composites using deep neural network (DNN),” Compos Struct, vol. 302, p. 116248, 2022.
  15. Zhou, W. Chen, C. Shen, L. Cheng, and M. Xia, “Intelligent machine fault diagnosis with effective denoising using EEMD-ICA-FuzzyEn and CNN,” Int J Prod Res, pp. 1–13, 2022.
  16. Xia, H. Shao, Z. Huang, Z. Zhao, F. Jiang, and Y. Hu, “Intelligent process monitoring of laser-induced graphene production with deep transfer learning,” IEEE Trans Instrum Meas, vol. 71, pp. 1–9, 2022.
  17. Xia, H. Shao, D. Williams, S. Lu, L. Shu, and C. W. de Silva, “Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning,” Reliab Eng Syst Saf, vol. 215, p. 107938, 2021. (ESI Hot Paper)
  18. Chen, Q. Li, C. Shen, J. Zhu, D. Wang, and M. Xia, “Adversarial domain-invariant generalization: A generic domain-regressive framework for bearing fault diagnosis under unseen conditions,” IEEE Trans Industr Inform, vol. 18, no. 3, pp. 1790–1800, 2021. (ESI Highly Cited Paper)
  19. Shao, M. Xia, J. Wan, and C. W. de Silva, “Modified stacked autoencoder using adaptive Morlet wavelet for intelligent fault diagnosis of rotating machinery,” IEEE/ASME Transactions on Mechatronics, vol. 27, no. 1, pp. 24–33, 2021. (ESI Highly Cited Paper)
  20. Xia, H. Shao, X. Ma, and C. W. de Silva, “A stacked GRU-RNN-based approach for predicting renewable energy and electricity load for smart grid operation,” IEEE Trans Industr Inform, vol. 17, no. 10, pp. 7050–7059, 2021. (ESI Highly Cited Paper)
  21. Wang, S. Lu, W. Huang, Q. Wang, S. Zhang, and M. Xia, “Efficient data reduction at the edge of industrial Internet of Things for PMSM bearing fault diagnosis,” IEEE Trans Instrum Meas, vol. 70, pp. 1–12, 2021.
  22. Shao, M. Xia, G. Han, Y. Zhang, and J. Wan, “Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer convolutional neural network and thermal images,” IEEE Trans Industr Inform, vol. 17, no. 5, pp. 3488–3496, 2020. (ESI Hot Paper)
  23. Wang, C. Shen, M. Xia, D. Wang, J. Zhu, and Z. Zhu, “Multi-scale deep intra-class transfer learning for bearing fault diagnosis,” Reliab Eng Syst Saf, vol. 202, p. 107050, 2020. (ESI Highly Cited Paper)
  24. Xia, T. Li, T. Shu, J. Wan, C. W. De Silva, and Z. Wang, “A two-stage approach for the remaining useful life prediction of bearings using deep neural networks,” IEEE Trans Industr Inform, vol. 15, no. 6, pp. 3703–3711, 2018. (ESI Highly Cited Paper)
  25. Xia, T. Li, L. Xu, L. Liu, and C. W. De Silva, “Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks,” IEEE/ASME transactions on mechatronics, vol. 23, no. 1, pp. 101–110, 2017. (ESI Hot Paper, among most popular papers of TMECH)

 

Selected Conference Proceedings

  1. Xing, T. Yan, and M. Xia, “A Unified Piecewise Modeling Framework for Battery Knee Point Detection and State of Health Estimation”, 2025 34th IEEE International Symposium on Industrial Electronics (ISIE), 2025. 
  2. Zhao and M. Xia, “An Unsupervised Knowledge and Data Dual-Driven Based Fault Diagnosis for Industrial Process”, 2025 34th IEEE International Symposium on Industrial Electronics (ISIE), 2025. 
  3. Wu and M. Xia, “Zero-Sample Fault Diagnosis for Bearings Using a Hierarchical Contrast Learning Approach”, 2025 34th IEEE International Symposium on Industrial Electronics (ISIE), 2025. 
  4. Y. F. Ugurluoglu, D. Williams and M. Xia, "Enhancing Genetic Algorithm-Based Process Parameter Optimisation Through Grid Search-Optimised Artificial Neural Networks, "2023 28th International Conference on Automation and Computing (ICAC), IEEE, 2023, pp. 1-6.
  5. M. Roberts, M. Xia, and A. Kennedy. "Data-driven Process Parameter Optimisation for Laser Wire Metal Additive Manufacturing." 2022 27th International Conference on Automation and Computing (ICAC). IEEE, 2022.
  6. H. Shao, W. Li, M. Xia*, C. Wang, Q. Guan and T. Xu, "Rotating Machinery Fault Classification using IWGAN-GP and Small Gray Images," 2021 16th International Conference on Computer Science & Education (ICCSE), 2021, pp. 222-227, doi: 10.1109/ICCSE51940.2021.9569392. (Best Paper Award)
  7. H. Cao, H. Shao, M. Xia, W. Luo, F. Zhu and D. Su, "Unsupervised Domain-shared Convolutional Neural Network for Bearing Fault Transfer Diagnosis," 2021 16th International Conference on Computer Science & Education (ICCSE), 2021, pp. 216-221, doi: 10.1109/ICCSE51940.2021.9569672.
  8. M. Xia, T. Li, L. Liu, L. Xu, S. Gao and C. W. de Silva, "Remaining useful life prediction of rotating machinery using hierarchical deep neural network," 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017, pp. 2778-2783, doi: 10.1109/SMC.2017.8123047.
  9. T. Li, M. Xia, J. Chen, S. Gao and C.W.de Silva, "A hexagonal grid-based sampling planner for aquatic environmental monitoring using unmanned surface vehicles," 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017, pp. 3683-3688, doi: 10.1109/SMC.2017.8123205.
  10. M. Xia and C. W. de Silva, “A Framework of Design Weakness Detection through Machine Health Monitoring for the Evolutionary Design Optimization of Multi-Domain Systems”, 2014 9th International Conference on Computer Science & Education, 2014, pp. 205-210, doi: 10.1109/ICCSE.2014.6926455. (Best Paper Award)