Maintenance is an important technical measure to maintain and restore the performance status of equipment and ensure the safety of the production process in industrial production,and is an indispensable part of predic...Maintenance is an important technical measure to maintain and restore the performance status of equipment and ensure the safety of the production process in industrial production,and is an indispensable part of prediction and health management.However,most of the existing remaining useful life(RUL)prediction methods assume that there is no maintenance or only perfect maintenance during the whole life cycle;thus,the predicted RUL value of the system is obviously lower than its actual operating value.The complex environment of the system further increases the difficulty of maintenance,and its maintenance nodes and maintenance degree are limited by the construction period and working conditions,which increases the difficulty of RUL prediction.An RUL prediction method for a multi-omponent system based on the Wiener process considering maintenance is proposed.The performance degradation model of components is established by a dynamic Bayesian network as the initial model,which solves the uncertainty of insufficient data problems.Based on the experience of experts,the degree of degradation is divided according to Poisson process simulation random failure,and different maintenance strategies are used to estimate a variety of condition maintenance factors.An example of a subsea tree system is given to verify the effectiveness of the proposed method.展开更多
Objective： To standardize pain management in the anesthesia recovery period and improve the effects of analgesia on acute postoperative pain. Methods： Using healthcare failure mode and effect analysis （HFMEA）, we ...Objective： To standardize pain management in the anesthesia recovery period and improve the effects of analgesia on acute postoperative pain. Methods： Using healthcare failure mode and effect analysis （HFMEA）, we analyzed the primary cause of patients＇ pain and subsequently determined the process and risk priority number （RPN）. Results： Actions were taken to improve patients＇ pain. After using HFMEA, the experimental group＇s visual analog scale （VAS） scores were lower than those of the control group at 1 h and at discharge from the post-anesthetic intensive care unit （PAICU）. The differences were statistically significant （P 〈 0.05）. Conclusions： The application of failure mode and effect analysis can relieve pain and improve the quality of nursing.展开更多
Membranes of polypropylene （PP）, PP coated with nano-A1203, PP electrospun with polyvinylidene fluoride- hexafluoropropylene （PVdF-HFP）, and trilayer laminates of polypropylene-polyethylene-polypropylene （PP/PE/P...Membranes of polypropylene （PP）, PP coated with nano-A1203, PP electrospun with polyvinylidene fluoride- hexafluoropropylene （PVdF-HFP）, and trilayer laminates of polypropylene-polyethylene-polypropylene （PP/PE/PP） were comparatively studied. Their physical properties were characterized by means of thermal shrinkage test, liquid electrolyte uptake, and field emission scanning electron microscopy （FESEM）. Results show that, for the different membranes as PP, PP coated with nanowA1203, PP electrospun with PVdF-HFP, and PP/PE/PP, the thermal shrinkages are 14%, 6%, 12.6%, and 13.3%, while the liquid electrolyte uptakes are 110%, 150%, 217%, and 129%, respectively. In addition, the effects on the performance of lithium-ion batteries （LiFePO4 and LiNil/3Col/3Mn1/302 as the cathode material） were investigated by AC impedance and galvanostatic charge/discharge test. It is found that PP coated with A1203 and PP electrospun with PVdF-HFP can effectively increase the wettability between the cathode material and liquid electrolyte, and therefore reduce the charge transfer resistance, which improves the capacity retention and battery performance.展开更多
基金financially supported by the National Key Research and Development Program of China(Grant No.2022YFC3004802)the National Natural Science Foundation of China(Grant Nos.52171287,52325107)+3 种基金High Tech Ship Research Project of Ministry of Industry and Information Technology(Grant Nos.2023GXB01-05-004-03,GXBZH2022-293)the Science Foundation for Distinguished Young Scholars of Shandong Province(Grant No.ZR2022JQ25)the Taishan Scholars Project(Grant No.tsqn201909063)the sub project of the major special project of CNOOC Development Technology,“Research on the Integrated Technology of Intrinsic Safety of Offshore Oil Facilities”(Phase I),“Research on Dynamic Quantitative Analysis and Control Technology of Risks in Offshore Production Equipment”(Grant No.HFKJ-2D2X-AQ-2021-03)。
文摘Maintenance is an important technical measure to maintain and restore the performance status of equipment and ensure the safety of the production process in industrial production,and is an indispensable part of prediction and health management.However,most of the existing remaining useful life(RUL)prediction methods assume that there is no maintenance or only perfect maintenance during the whole life cycle;thus,the predicted RUL value of the system is obviously lower than its actual operating value.The complex environment of the system further increases the difficulty of maintenance,and its maintenance nodes and maintenance degree are limited by the construction period and working conditions,which increases the difficulty of RUL prediction.An RUL prediction method for a multi-omponent system based on the Wiener process considering maintenance is proposed.The performance degradation model of components is established by a dynamic Bayesian network as the initial model,which solves the uncertainty of insufficient data problems.Based on the experience of experts,the degree of degradation is divided according to Poisson process simulation random failure,and different maintenance strategies are used to estimate a variety of condition maintenance factors.An example of a subsea tree system is given to verify the effectiveness of the proposed method.
文摘Objective： To standardize pain management in the anesthesia recovery period and improve the effects of analgesia on acute postoperative pain. Methods： Using healthcare failure mode and effect analysis （HFMEA）, we analyzed the primary cause of patients＇ pain and subsequently determined the process and risk priority number （RPN）. Results： Actions were taken to improve patients＇ pain. After using HFMEA, the experimental group＇s visual analog scale （VAS） scores were lower than those of the control group at 1 h and at discharge from the post-anesthetic intensive care unit （PAICU）. The differences were statistically significant （P 〈 0.05）. Conclusions： The application of failure mode and effect analysis can relieve pain and improve the quality of nursing.
基金supported by the Fundamental Research Funds for the Central Universities of China(No.FRF-MP-12-005B)the Project on International Cooperation Research with Johnson Controls Battery Group,Inc.
文摘Membranes of polypropylene （PP）, PP coated with nano-A1203, PP electrospun with polyvinylidene fluoride- hexafluoropropylene （PVdF-HFP）, and trilayer laminates of polypropylene-polyethylene-polypropylene （PP/PE/PP） were comparatively studied. Their physical properties were characterized by means of thermal shrinkage test, liquid electrolyte uptake, and field emission scanning electron microscopy （FESEM）. Results show that, for the different membranes as PP, PP coated with nanowA1203, PP electrospun with PVdF-HFP, and PP/PE/PP, the thermal shrinkages are 14%, 6%, 12.6%, and 13.3%, while the liquid electrolyte uptakes are 110%, 150%, 217%, and 129%, respectively. In addition, the effects on the performance of lithium-ion batteries （LiFePO4 and LiNil/3Col/3Mn1/302 as the cathode material） were investigated by AC impedance and galvanostatic charge/discharge test. It is found that PP coated with A1203 and PP electrospun with PVdF-HFP can effectively increase the wettability between the cathode material and liquid electrolyte, and therefore reduce the charge transfer resistance, which improves the capacity retention and battery performance.
