[1] Jing Luyang, Zhao Ming*, Li Pin, et al. A Convolutional Neural Network Based Feature Learning and Fault Diagnosis Method for the Condition Monitoring of Gearbox[J]. Measurement, 2017, 111: 1-10. (SCI)（ESI熱點(diǎn)論文、ESI高被引論文）
[2] Jing Luyang, Wang Taiyong*, Zhao Ming, et al. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox[J]. Sensors, 2017, 17(2): 414-429. (SCI)（ESI高被引論文）
[3] Xu Weixiao, Shen Yujie, Jing Luyang*. A Life Prediction Method based on MDFF and DITCN-ABiGRU Mixed Network Model[J]. Heliyon, 2024, 10(2): 24299. (SCI)
[4] 王曉昆,井陸陽*,白曉瑞,等.仿真數(shù)據(jù)驅(qū)動(dòng)的起重機(jī)鋼絲繩斷絲定量識(shí)別方法[J].機(jī)電工程,2024,41(01):43-51.(中文核心) (榮獲《機(jī)電工程》雜志2024年度優(yōu)秀論文)
[5] 張政君,井陸陽*,徐衛(wèi)曉,等.基于時(shí)頻圖與雙通道卷積神經(jīng)網(wǎng)絡(luò)的軸承故障識(shí)別模型[J].機(jī)電工程,2023,40(12):1889-1897. (中文核心)
[6] 徐衛(wèi)曉*,井陸陽,孫顯斌等.基于MDFF和DCNN-SVM混合網(wǎng)絡(luò)的滾動(dòng)軸承故障診斷研究[J].制造技術(shù)與機(jī)床,2023(05):13-20.(中文核心)
[7] 陳榮信,井陸陽*,白曉瑞等.基于殘差網(wǎng)絡(luò)的鋼絲繩損傷圖像定量識(shí)別[J].機(jī)床與液壓,2023,51(12):24-29.(中文核心)
[8] Zhang Y, Feng Z, Shi S, Dong Z, Zhao L, Jing L*, Tan J. A quantitative identification method based on CWT and CNN for external and inner broken wires of steel wire ropes[J]. Heliyon, 2022, 8(11): e11623. (SCI)
[9] Zhang Y*, Han J, Jing L, Wang C, Zhao L. Intelligent Fault Diagnosis of Broken Wires for Steel Wire Ropes Based on Generative Adversarial Nets. Applied Sciences. 2022, 12(22):11552. (SCI)
[10] Xu Weixiao, Jing Luyang*, Jiwen Tan, et al. A Multimodel Decision Fusion Method Based on DCNN-IDST for Fault Diagnosis of Rolling Bearing [J]. Shock and Vibration, 2020, Article ID 8856818, 12. (SCI)
[11] Zhang Yiqing, Jing Luyang, Tan Jiwen*, et al. A Comparative Study of the Magnetic Concentrating Sensor and the Hall Array Sensor for Damage Detection of Steel Wire Ropes [J]. Materials Research Express , 2020, 9: 1-15 (SCI)
[12] Zhang Yiqing, Jing Luyang*, Xu Weixiao, et al. A Sensor for Broken Wire Detection of Steel Wire Ropes Based on the Magnetic Concentrating Principle[J]. Sensors, 2019, 19(17): 3763-3777. (SCI)
[13] 張立智, 井陸陽*, 徐衛(wèi)曉, 等. CNN 和 D-S 證據(jù)理論相結(jié)合的齒輪箱復(fù)合故障診斷研究[J]. 機(jī)械科學(xué)與技術(shù), 2019, 38(10): 1582-1588.(中文核心)
[14] 張立智, 井陸陽*, 徐衛(wèi)曉, 等. 基于卷積降噪自編碼器和CNN的滾動(dòng)軸承故障診斷[J]. 組合機(jī)床與自動(dòng)化加工技術(shù), 2019, (06): 58-62.(中文核心)
[15] Jing Luyang, Chen Dongxiang, Wang Taiyong*, et al. Research on SVM based Diagnosis System for Oil Tubing[J]. Key Engineering Materials, 2016, 693: 1405-11. (EI)
[16] Jing Luyang,, Wang TaiYong*, Chen Dongxiang, et al. Design and Implementation of Online Monitoring and Remote Diagnostic System for CNC Machine Tools[J]. Advanced Materials Research, 2013, 819: 136-139. (EI)
[17] 井陸陽*, 王太勇, 陳東祥, 等. 數(shù)控機(jī)床多參數(shù)在線監(jiān)測診斷系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn)[J]. 制造業(yè)自動(dòng)化, 2013, 35(11): 178-180. (中文核心)
[18] 井陸陽, 王太勇*, 陳東祥, 等. 數(shù)控裝備微弱故障早期辨識(shí)及遠(yuǎn)程智能維護(hù)理論與系統(tǒng)研究[C]. 全國設(shè)備監(jiān)測診斷與維護(hù)學(xué)術(shù)會(huì)議、全國設(shè)備故障診斷學(xué)術(shù)會(huì)議暨2014年全國設(shè)備診斷工程會(huì)議, 2014, 33(S).
