国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

In order to solve the problem of failure of patients with failures caused by the uncertainty of lithium-ion batteries in medical electronic equipments, a set of prognostics and health management (PHM) systems for lithium-ion batteries in medical electronic equipment was proposed. An experiment platform for data testing and degradation status simulation of lithium batteries for medical electronic equipment was built. In order to reflect the health status of lithium-ion batteries for medical electronic devices, the four health factors of lithium batteries are extracted as characteristics of the degradation status of lithium-ion batteries for medical electronic devices, and they are passed through a nonlinear auto-regressive with exogenous inputs (NARX) neural network. The data of the health factors were trained and used for capacity estimation after training, and the equal interval discharge time series could be used to better characterize the lithium battery health status. In order to improve the precision of the Particle Filter-PF and more accurately predict the Reamaling Useful Life-RUL, the Artificial Immune Particle Filter (AIPF) and the Empirical Model for Lithium The battery performs the remaining life prediction, and compares the PF prediction result with the AIPF prediction result, and finds that the AIFF prediction is more accurate, indicating that AIFF effectively inhibits the particle degradation problem in the PF re-sampling process, and verifies the failure prediction of the lithium ion battery for medical electronic equipment. Health management system feasibility and enforceability.