[1]张英波,贾云献,邱国栋,等.基于油液中金属浓度梯度特征的滤波剩余寿命预测模型[J].系统工程理论与实践,2014,34(6):1620-1625.
ZHANG Yingbo, JIA Yunxian, QIU Guodong, et al. Stochastic Filtering Residual Useful Life Prediction Model Based on Metal Concentration Gradient in Lubricant[J]. Systems Engineering Theory & Practice, 2014,34(6):1620-1625.
[2]闫书法,马彪,郑长松,等.非线性状态监测数据下的磨损定位与状态识别[J].吉林大学学报(工学版),2019,49(2):359-365.
YAN Shufa, MA Biao, ZHENG Changsong, et al. Wear Localization and Identification under Nonlinear Condition Monitoring Data[J]. Journal of Jilin University (Engineering and Technology Edition), 2019, 49(2):359-365.
[3]闫书法,马彪,郑长松.基于油液光谱分析的综合传动视情维护研究[J].光谱学与光谱分析,2019,39(11):3470-3474.
YAN Shufa, MA Biao, ZHENG Changsong. Condition-based Maintenance for Power-shift Steering Transmission Based on Oil Spectral Analysis[J]. Spectroscopy and Spectral Analysis, 2019,39(11):3470-3474.
[4]徐斌,温广瑞,苏宇,等.多层次信息融合在铁谱图像磨粒识别中的应用[J].光学精密工程,2018,26(6):1551-1560.
XU Bin, WEN Guangrui, SU Yu, et al. Application of Multi-level Information Fusion for Wear Particle Recognition of Ferrographic Images[J]. Optics and Precision Engineering,2018,26(6):1551-1560.
[5]WU T H, WU H K, DU Y, et al. Progress and Trend of Sensor Technology for On-line Oil Monitoring[J]. Science China, 2013, 56(12):2914-2926.
[6]郝延龙,何红坤,常青,等.基于显微图像识别的在线润滑油中磨粒分析方法[J].润滑与密封,2016,41(5):59-64.
HAO Yanlong, HE Hongkun, CHANG Qing, et al. On-line Analysis for Particles in Lubricating Oil Based on Micro-image Recognition Method[J]. Lubrication Engineering, 2016,41(5):59-64.
[7]李健乐,赵利平,梁义维.基于光阻法颗粒计数器的气泡识别方法[J].仪表技术与传感器,2018(9):29-32.
LI Jianyue, ZHAO Liping, LIANG Yiwei. Bubble Recognition Method Based on Particle Counter of Light-blocking Theory[J]. Instrument Technique and Sensor, 2018(9):29-32.
[8]吕纯,张培林,吴定海,等.基于超声传感器的油液磨粒在线监测系统的研究[J].机床与液压,2016,44(7):73-75.
LYU Chun, ZHANG Peilin, WU Dinghai, et al. Research on Online Monitoring System for Oil Wear Debris Based on Ultrasonic Sensor[J]. Machine Tool & Hydraulics, 2016,44(7):73-75.
[9]杨昊,孙衍山,李健,等.滑油磨粒信号的变分模态分解和概率密度估计[J].仪器仪表学报,2018,39(4):99-106.
YANG Hao, SUN Yanshan, LI Jian, et al. Variational Mode Decomposition and Probability Density Estimation of Lubricating Oil Debris Detection Signal[J]. Chinese Journal of Scientific Instrument, 2018,39(4):99-106.
[10]史皓天,张洪朋,王文琪,等.高精度磨粒检测传感器的设计及研究[J].光学精密工程,2019,27(9):2043-2052.
SHI Haotian,ZHANG Hongpeng,WANG Wenqi,et al. Design and Research of High-sensitivity Wear Debris Detection Sensor[J]. Optics and Precision Engineering, 2019, 27(9):2043-2052.
[11]XIE Yucai, SHI Haotian, ZHANG Hongpeng, et al. A Bridge-type Inductance Sensor with a Two-stage Filter Circuit for High-precision Detection of Metal Debris in the Oil[J]. IEEE Sensors Journal, 2021, 21(16):17738-17748.
[12]DU L, ZHU X L, HAN Y, et al. Improving Sensitivity of an Inductive Pulse Sensor for Detection of Metallic Wear Debris in Lubricants Using Parallel LC Resonance Method[J]. Measurement Science & Technology, 2013, 24(7):660-664.
[13]史皓天, 张洪朋, 顾长智,等.电感-电容双模式液压油污染物检测传感器[J].机械工程学报,2020,56(2):20-26.
SHI Haotian, ZHANG Hongpeng, GU Changzhi, et al. Inductance-capacitance Dual Mode Sensor for the Detection of Contaminants in Hydraulic Oil [J]. Journal of Mechanical Engineering,2020,56(2):20-26.
[14]萧红,周威,罗久飞,等.一种高梯度静磁场感应式全流量磨粒监测传感器[J].仪器仪表学报,2020,41(6):10-18.
XIAO Hong, ZHOU Wei, LUO Jiufei, et al. AnInductive Sensor Based on the High-gradient Static Magnetic Field for Full Flow Debris Monitoring[J]. Chinese Journal of Scientific Instrument, 2020,41(6):10-18.
[15]LI C, LIANG M. Extraction of Oil Debris Signature Using Integral Enhanced Empirical Mode Decomposition and Correlated Reconstruction[J]. Measurement Science & Technology, 2011, 22(8):085701.
[16]LIU H, LI T, HONG W, et al. Using Multi-window Correlation to Improve Sensitivity and Adaptability for Oil Debris Detections[J]. Measurement, 2021, 176:109236.
[17]DU L, ZHE J. A High Throughput Inductive Pulse Sensor for Online Oil Debris Monitoring[J]. Tribology International, 2011, 44(2):175-179.
[18]刘恩辰,张洪朋,吴瑜,等.油液过流速度对船舶液压油检测精度的影响[J].光学精密工程,2016,24(3):533-539.
LIU Enchen, ZHANG Hongpeng, WU Yu, et al. Effect of Oil Velocity on Sensitivity of Micron Metal Particle Detection by Inductive Sensor[J]. Optics and Precision Engineering, 2016,24(3):533-539.
[19]DU L, JIANG Z. Parallel Sensing of Metallic Wear Debris in Lubricants Using Undersampling Data Processing[J]. Tribology International, 2012, 53:28-34.
[20]曾霖. 基于微阻抗分析的船机油液污染物区分检测机理研究[D].大连:大连海事大学,2019.
ZENG Lin. Distinguishing Detection Mechanism of Hydraulic Oil Contaminants Based on Micro Impedance Analysis [D]. Dalian:Dalian Maritime University,2019.
[21]WU Yu, ZHANG Hongpeng, SUN Yuqing, et al. Research on the Influence of Velocity on the Sensitivity of Inductive Sensor[J]. Journal of Engineering Research, 2017, 5(2):129-140.
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