We have produced stretchable lithium-ion batteries (LIBs) using the concept of kirigami, i.e., a combination of folding and cutting. The designated kirigami patterns have been discovered and implemented to achieve great stretchability (over 150%) to LIBs that are produced by standardized battery manufacturing. It is shown that fracture due to cutting and folding is suppressed by plastic rolling, which provides kirigami LIBs excellent electrochemical and mechanical characteristics. The kirigami LIBs have demonstrated the capability to be integrated and power a smart watch, which may disruptively impact the field of wearable electronics by offering extra physical and functionality design spaces.

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In the age of Internet of Things and Industrial 4.0, prognostic and health management (PHM) systems are used to collect massive real-time data from mechanical equipment. PHM big data has the characteristics of large-volume, diversity, and high-velocity. Effectively mining features from such data and accurately predicting the remaining useful life (RUL) of the rotating components with new advanced methods become issues in PHM. Traditional data driven prognostics is based on shallow learning architectures, requires establishing explicit model equations and much prior knowledge about signal processing techniques and prognostic expertise, and therefore is limited in the age of big data. This paper presents a deep learningbased approach for RUL prediction of rotating components with big data. The presented approach is tested and validated using data collected from a gear test rig and bearing run-to-failure tests and compared with existing PHM methods. The test results show the promising RUL prediction performance of the deep learning-based approach.

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