State of Health Monitoring and Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Integrated Model

Zhang, Jun; Zhehui, Wang; Sun, Chuang; Shi, Qiyang; Jia, Zhiwei

doi:10.1109/wcmeim56910.2022.10021408

Cited by 1 publication

(1 citation statement)

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“…Health indicator construction is the quantitative expression of the health state of mechanical equipment, which can be divided into two categories according to physical significance, namely, physical health indicators and virtual health indicators. Physical health indicators refer to the time domain, frequency domain, and time-frequency domain characteristics of mechanical equipment, which have physical significance but poor monotonicity and a poor trend and are seriously affected by noise [74]. Virtual health indicators refer to a type of indicators representing the running state of mechanical equipment, being obtained from the fusion of multiple physical health indicators or sensor data, which have no physical significance but can reflect the degradation trend and achieve good results in terms of monotonicity, trends, and scale similarity [75].…”

Section: State Of Research On Dynamic Data-driven Rul Prediction Methodsmentioning

confidence: 99%

Summarization of Remaining Life Prediction Methods for Special Power Plants

Liang,

Li,

Zhao

et al. 2023

Applied Sciences

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With continuous improvements in integration, totalization and automation, remaining useful life predictions of mechanical equipment have become a key feature of technology and core element of equipment prediction and health management. The traditional method based on degradation mechanisms is not fully capable of predicting remaining useful life, especially for special power plants that use industrial transmissions, barrel launchers, etc. The expected service requirements are higher for condition monitoring and remaining service life prediction. The effective prediction of the remaining useful life of such special power plants is a major challenge and technical bottleneck in the industrial field and national defense equipment construction. This paper analyzes and expands on the research on the remaining life prediction methods for special power plants and analyzes the remaining life prediction methods of existing dynamic models, as well as data-driven and data–model fusion drives, and specific ideas for future research and development in four aspects, including remaining useful life prediction tests supplemented with soft measurements. Additionally, future research directions for the remaining life prediction of special power plants are provided.

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Section: State Of Research On Dynamic Data-driven Rul Prediction Methodsmentioning

confidence: 99%