何建樑，男,，1993年7月生,，工學(xué)博士。2019年畢業(yè)于河南師范大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)專業(yè),，獲理學(xué)碩士學(xué)位,，導(dǎo)師毛文濤教授，2020年-2024年,，在南京理工大學(xué)機(jī)械工程學(xué)院攻讀博士學(xué)位,，獲工學(xué)博士學(xué)位，導(dǎo)師王禹林教授,。研究方向包括：數(shù)據(jù)驅(qū)動(dòng)的機(jī)械設(shè)備運(yùn)行狀態(tài)監(jiān)測(cè)系統(tǒng)構(gòu)建,、機(jī)械設(shè)備切削加工故障診斷、云邊協(xié)同大數(shù)據(jù)驅(qū)動(dòng)的深度學(xué)習(xí)狀態(tài)識(shí)別方法等,，包括多源數(shù)據(jù)采集程序開發(fā),，振動(dòng)信號(hào)分析處理，深度學(xué)習(xí)網(wǎng)絡(luò)建模,，工業(yè)大數(shù)據(jù)存儲(chǔ)環(huán)境實(shí)現(xiàn),，運(yùn)行狀態(tài)監(jiān)測(cè)軟件系統(tǒng)構(gòu)建等。主持江蘇省研究生科研與實(shí)踐創(chuàng)新計(jì)劃項(xiàng)目1項(xiàng),，參與國(guó)家自然科學(xué)基金面上項(xiàng)目,、國(guó)家科技重大專項(xiàng)“04專項(xiàng)”、國(guó)家重點(diǎn)研發(fā)計(jì)劃“高性能制造技術(shù)與重大裝備”重點(diǎn)專項(xiàng)等6余項(xiàng),，在Journal of Intelligent Manufacturing,，Mechanical Systems and Signal Processing，IEEE Transactions on Instrumentation and Measurement等國(guó)際學(xué)術(shù)刊物及會(huì)議發(fā)表論文5余篇,。近年來?yè)?dān)任Journal of Intelligent Manufacturing,，Engineering Applications of Artificial Intelligence等學(xué)術(shù)期刊論文評(píng)審。

發(fā)表論文：

[1]Jianliang He(何建樑), Yuxin Sun, Chen Yin, et al. Cross-domain adaptation network based on attention mechanism for tool wear prediction[J].Journal of Intelligent Manufacturing(中科院 1 區(qū) TOP SCI, IF =8.3),2022:1-23.

[2]Jianliang He(何建樑), Yadong Xu, Yi Pan, Yulin Wang. Adaptive Weighted Generative Adversarial Network with Attention Mechanism: A Transfer Data Augmentation Method for Tool Wear Prediction[J]. Mechanical Systems and Signal Processing (Accepted) 2024 (中科院 1 區(qū) SCI, IF =8.4).

[3]Jianliang He(何建樑), Chen Yin, Yan He, et al. Deep multi-task network based on sparse feature learning for tool wear prediction[J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science(中科院4區(qū) SCI, IF =2.0), 2022: 09544062221116224.

[4]Yuxin Sun, Jianliang He(何建樑), Haifeng Ma, et al. Online chatter detection considering beat effect based on Inception and LSTM neural networks[J]. Mechanical Systems and Signal Processing(中科院 1 區(qū) SCI, IF =8.4), 2023, 184: 109723.

[5]Wentao Mao, Jianliang He(何建樑), Ming J. Zuo. Predicting remaining useful life of rolling

bearings based on deep feature representation and transfer learning[J]. IEEE Transactions on Instrumentation and Measurement(中科院 2 區(qū) SCI, IF =5.6), 2019, 69(4): 1594-1608.

[6]Wentao Mao, Jianliang He(何建樑), Yuan Li, et al. Bearing fault diagnosis with auto-encoder extreme learning machine: A comparative study[J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2017, 231(8): 1560-1578.(SCI , 4區(qū))

[7]Wentao Mao, Jianliang He(何建樑), Xizheng Cao, et al. Predicting Remaining Useful Life of Rolling Bearings based on Deep Feature Representation and LSTM Neural Network, Advances in Mechanical Engineering. (SCI , 3區(qū))

科研項(xiàng)目：

[1]江蘇省研究生科研與實(shí)踐創(chuàng)新計(jì)劃(KYCX23_0424)[主持]   2023.4–2024.5

基于云邊協(xié)同的數(shù)控機(jī)床銑削刀具壽命預(yù)測(cè)方法研究

[2]國(guó)家重點(diǎn)研發(fā)計(jì)劃(2021YFB2012104)[主要參與]   2022.12–2025.11

高端裝備協(xié)同智能故障診斷理論與預(yù)測(cè)方法—高端裝備退化評(píng)估與數(shù)模聯(lián)動(dòng)剩余壽命預(yù)測(cè) 

[3]國(guó)家自然基金面上項(xiàng)目(52075267)[主要參與]   2021.1-2024.12

基于深度學(xué)習(xí)機(jī)床故障小子樣時(shí)序信息生成融合與遷移診斷預(yù)警

[4]國(guó)家重點(diǎn)研發(fā)計(jì)劃(2021YFB2012104)[主要參與]   2021.11–2024.12

傳感器在諧波減速器和 RV 減速器應(yīng)用驗(yàn)證—工業(yè)機(jī)器人減速器狀態(tài)監(jiān)測(cè)傳感器關(guān)鍵技術(shù)

[5]國(guó)家科技重大專項(xiàng)04專項(xiàng)(2018ZX04002001)[參與] 2018.1–2019.12

直升機(jī)發(fā)動(dòng)機(jī)空間動(dòng)力傳動(dòng)單元體高精高效智能化加工應(yīng)用示范-基于制造大數(shù)據(jù)的生產(chǎn)線智能化提升關(guān)鍵技術(shù)研究

[6]國(guó)家科技重大專項(xiàng)04專項(xiàng)(2018ZX04041001)[參與] 2018.1–2020.12

整體硬質(zhì)合金刀具五軸磨削柔性制造單元研制與示范應(yīng)用-基于加工大數(shù)據(jù)的制造單元運(yùn)行狀態(tài)監(jiān)測(cè)關(guān)鍵技術(shù)研究

聯(lián)系方式

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