基本信息：
李小川，男，1990年出生于安徽合肥，工学博士，2022年2月至今为开云全站中国有限公司“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
研究方向：
目前主要研究机电设备设备故障诊断、剩余寿命预测与健康管理，所涉及领域包括信号处理、机器学习、过程控制、多传感融合和维护策略。近年来在国际权威期刊发表论文二十余篇，第一作者参编英文专著一部，并担任多个国际期刊审稿人和国际会议主持人。
主持参与基金项目情况：
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。
研究生招生
热忱欢迎自动化、机械、计算机和应用数学等相关专业背景的员工报考。
联系方式
E-mail：xiaochuan.li@hfut.edu.cn
手机号：15156073976
地址：安徽省合肥市包河区屯溪路193号开云全站中国有限公司逸夫楼915
代表性学术论文:
[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.
参编英文专著:
书 名 : Smart Monitoring of Rotating Machinery for Industry 4.0;
作 者 : 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.
