Faculty & Staff



李小川,男,1990年出生于安徽合肥,工学博士,20222月至今为合肥工业大学“黄山学者学术骨干”副教授/副研究员。2018-2019年英国德蒙福特大学博士后,2019-2021年英国德蒙福特大学讲师(永久教职)。英国高等教育协会Associate Fellow。发表故障诊断、剩余寿命预测与健康管理相关学术论文




1. 壳牌石油公司(英国)燃气轮机与压缩机故障诊断科研项目,2015-2018,参研。

2. 壳牌石油公司(英国)与德蒙福特大学联合研究项目-基于机器学习的大型旋转机械剩余寿命与健康管理,2018-2019,主持。








[1] Li, X., Mba, D., Lin, T., Yang, Y. and Loukopoulos, P., 2021. Just-in-time learning based probabilistic gradient boosting tree for valve failure prognostics. Mechanical Systems and Signal Processing, 150, p.107253.

[2] Li, X., Yang, Y., Bennett, I. and Mba, D., 2019. Condition monitoring of rotating machines under time-varying conditions based on adaptive canonical variate analysis. Mechanical Systems and Signal Processing, 131, pp.348-363.

[3] Li, X., Yang, X., Yang, Y., Bennett, I. and Mba, D., 2019. A novel diagnostic and prognostic framework for incipient fault detection and remaining service life prediction with application to industrial rotating machines. Applied Soft Computing, 82, p.105564.

[4] Li, X., Yang, X., Yang, Y., Bennett, I. and Mba, D., 2020. An intelligent diagnostic and prognostic framework for large-scale rotating machinery in the presence of scarce failure data. Structural Health Monitoring, 19(5), pp.1375-1390.

[5] Li, X., Yang, X., Yang, Y., Bennett, I., Collop, A. and Mba, D., 2019. Canonical variate residuals-based contribution map for slowly evolving faults. Journal of Process Control, 76, pp.87-97.

[6] Li, X., Duan, F., Loukopoulos, P., Bennett, I. and Mba, D., 2018. Canonical variable analysis and long short-term memory for fault diagnosis and performance estimation of a centrifugal compressor. Control Engineering Practice, 72, pp.177-191.

[7] Li, X., Elasha, F., Shanbr, S. and Mba, D., 2019. Remaining useful life prediction of rolling element bearings using supervised machine learning. Energies, 12(14), p.2705.

[8] Li, X., Mba, D., Diallo, D. and Delpha, C., 2019. Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults. Energies, 12(4), p.726.

[9] Li, X., Duan, F., Bennett, I. and Mba, D., 2018. Canonical variate analysis, probability approach and support vector regression for fault identification and failure time prediction. Journal of Intelligent & Fuzzy Systems, 34(6), pp.3771-3783.

[10] Li, X., Duan, F., Mba, D. and Bennett, I., 2017. Multidimensional prognostics for rotating machinery: A review. Advances in Mechanical Engineering, 9(2), p.1687814016685004.

[11] Li, X., Mba, D., Okoroigwe, E. and Lin, T., 2021. Remaining service life prediction based on gray model and empirical Bayesian with applications to compressors and pumps. Quality and Reliability Engineering International, 37(2), pp.681-693.

[12] Yang, X., Fang, Z., Yang, Y., Mba, D. and Li, X., 2019. A novel multi-information fusion grey model and its application in wear trend prediction of wind turbines. Applied Mathematical Modelling, 71, pp.543-557.

[13] Loukopoulos, P., Zolkiewski, G., Bennett, I., Sampath, S., Pilidis, P., Li, X. and Mba, D., 2019. Abrupt fault remaining useful life estimation using measurements from a reciprocating compressor valve failure. Mechanical Systems and Signal Processing, 121, pp.359-372.

[14] Wu, Y., Lv, W., Li, Z., Chang, J., Li, X. and Liu, S., 2022. Unsupervised domain adaptation for vibration-based robotic ground classification in dynamic environments. Mechanical Systems and Signal Processing, 169, p.108648.


: Smart Monitoring of Rotating Machinery for Industry 4.0;

: Chaari, F., Chiementin, X., Zimroz, R., Bolaers, F., Haddar, M. 年份:2022. 出版社:Springer. (Book, EI indexed)

参编章节名称:A Tutorial on Canonical Variate Analysis for Diagnosis and Prognosis. 参编章节作者: Li, X., Lin, T. and Mba, D.