TY - GEN
T1 - Research on Engine Lubricant Detection Technology Based on On-line Visual Technology
AU - Li, Yingshun
AU - Zuo, Yang
AU - Yi, Xiaojian
AU - Liu, Haiyang
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The abrasive grains contained in the engine lubricant can not only characterize the quality of the lubricant used, but also indirectly characterize the technical status of the machine operation. Therefore, the detection of abrasive particles in engine lubricants can not only find the quality changes of lubricants, is conducive to the realization of "oil on demand", reduce costs, but also through the detection of lubricant mill particles to predict machine failure, to ensure the safe operation of the machine. Monitoring the state of lubricants, not only can timely find changes in the quality of lubricants, so that on-demand replacement of lubricants, reduce operating costs, more important is that the grinding grain in the lubricant contains important machine status information, through the oil mill particle detection information can predict the technical status of the machine in advance, and then timely detection of signs of failure and forecast equipment may occur, targeted maintenance and repair.
AB - The abrasive grains contained in the engine lubricant can not only characterize the quality of the lubricant used, but also indirectly characterize the technical status of the machine operation. Therefore, the detection of abrasive particles in engine lubricants can not only find the quality changes of lubricants, is conducive to the realization of "oil on demand", reduce costs, but also through the detection of lubricant mill particles to predict machine failure, to ensure the safe operation of the machine. Monitoring the state of lubricants, not only can timely find changes in the quality of lubricants, so that on-demand replacement of lubricants, reduce operating costs, more important is that the grinding grain in the lubricant contains important machine status information, through the oil mill particle detection information can predict the technical status of the machine in advance, and then timely detection of signs of failure and forecast equipment may occur, targeted maintenance and repair.
KW - Fault prediction
KW - Health management
KW - Online visualization
KW - Wear particles
UR - http://www.scopus.com/inward/record.url?scp=85126217720&partnerID=8YFLogxK
U2 - 10.1109/SDPC52933.2021.9563580
DO - 10.1109/SDPC52933.2021.9563580
M3 - Conference contribution
AN - SCOPUS:85126217720
T3 - Proceedings of 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021
SP - 87
EP - 93
BT - Proceedings of 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021
A2 - Fu, Xuyun
A2 - Deng, Shengcai
A2 - Cabrera, Diego
A2 - Zhang, Yongjian
A2 - Pu, Zhiqiang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021
Y2 - 13 August 2021 through 15 August 2021
ER -