馬萍,女,中共黨員,工學博士,博士生導師。現為新疆大學自動化系副教授,新疆自動化學會理事,中國自動化學會會員。原為新疆大學電氣工程學院自動化系教師,後因院系調整,隨自動化系調入新疆大學智慧型科學與技術學院(未來技術學院);為Expert System with Applications、ISA Transactions、Measurement、中國電機工程學報、振動與衝擊等期刊的審稿專家。主要研究方向包括:工業人工智慧、裝備智慧型運維、故障診斷、機器視覺、預測性維護、工業大數據、健康監測與維護決策等。主要講授本科課程《自動控制理論》、《新能源發電技術》,研究生課程《複雜網路算法與套用》、《動態系統故障診斷技術》等。近5年主持國家自然科學基金項目1項,中國博士後自然科學基金面上資助項目1項,新疆維吾爾自治區青年基金項目1項,橫向項目1項。入選自治區“天山英才-青年托舉人才”計畫、“天山青年”計畫、“天池博士”計畫、“優秀博士後特別資助研究人員”計畫。參與國家自然科學基金項目2項,橫向項目多項。以第一作者或通訊作者發表論文50餘篇,其中SCI/EI論文30餘篇。獲中國科學技術信息研究所2023年度“領跑者5000——中國精品科技期刊頂尖學術論文”,獲2023年新疆維吾爾自治區科學技術(自然科學)獎二等獎(排名第三)。
(1)Ma Ping, Li Guangfu, Zhang Hongli, et al. Prediction of Remaining Useful Life of Rolling Bearings Based on Multiscale Efficient Channel Attention CNN and Bidirectional GRU[J]. IEEE Transactions on Instrumentation and Measurement,2024, 73:2508413.(SCI)
(2)Ma Ping, Liang Weilong, Zhang Hongli, et al. Multiscale permutation entropy based on natural visibility graph and its application to rolling bearing fault diagnosis.Structural Health Monitoring, 2024. doi:10.1177/14759217241229999.(SCI)
(3)Gong Fengjin,Ma Ping*, Wang Nini, Zhang Hongli,et al. Cross-device fault diagnosis of rolling bearings using domain generalization and dynamic model. Journal of Vibration and Control. 2024. doi:10.1177/10775463241256253.(SCI)
(4)Wang Nini,Ma Ping*, Wang Xiaorong, et al. Detection of unknown bearing faults using re-weighted symplectic geometric node network characteristics and structure analysis[J]. Expert Systems with Applications, 2023, 215: 119304.(SCI)
(5)Ma Ping, Zhang Hongli, Wang Cong. Adaptive dynamic mode decomposition and its application in rolling bearing compound fault diagnosis[J]. Structural Health Monitoring, 2022: 14759217221095729.(SCI)
(6)Ma Ping,Zhang Zhou, Zhang Hongli, et al. Compound fault diagnosis of rolling bearing under variable speed based on generalized demodulation transformation and symplectic geometric mode decomposition[J].Journal of Vibration and Control, 2022: 10775463221082924.(SCI)
(7)Ma Ping;Zhang Hongli;Fan Wenhui;Wang Cong;A diagnosis framework based on domain adaptation for bearing fault diagnosis across diverse domains.ISA Transactions, 2020, 99: 465-487.(SCI)
(8)Ma Ping;Zhang Hongli;Fan Wenhui;Wang Cong;Early fault diagnosis of bearing based on frequency band extraction and improved tunable Q-factor wavelet transform,Measurement, 2019,137:189-202.(SCI)
(9)Ma Ping,Zhang Hongli, Fan Wenhui, et al. A novel bearing fault diagnosis method based on 2D image representation and transfer learning-convolutional neural network[J].Measurement Science and Technology, 2019, 30(5): 055402.(SCI)
(10)Ma Ping,Zhang Hongli, Fan Wenhui, et al. Early fault detection of bearings based on adaptive variational mode decomposition and local tangent space alignment.Engineering Computations,2019, 36(2):509-532.(SCI)
(15)Ma Ping,Zhang Hongli, Fan Wenhui, et al. Fault diagnosis using an improved fusion feature based on manifold learning for wind turbine transmission system[J].Journal of Vibroengineering, 2019, 21(7): 1859-1874.(EI)
(16)Ma Ping, Zhang Hongli, Fan Wenhui, et al. Novel bearing fault diagnosis model integrated with dual-tree complex wavelet transform, permutation entropy and optimized FCM[J].Journal of Vibroengineering, 2018, 20(2): 891-908.(EI)
(18)Wang Nini,Ma Ping* Zhang H, et al. Fault feature enhancement and diagnosis of rolling bearing based on complex network[C]//2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS). IEEE, 2021: 1-6.(EI會議)
(19)Ma Ping,Zhang Hongli, Wang Cong. Improved Transfer Component Analysis and It Application for Bearing Fault Diagnosis Across Diverse Domains[C]//2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS). IEEE, 2019: 501-506.(EI會議)