Development of PHM Algorithm of e-Latch to Prepare for the Era of Autonomous Driving
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Abstract
In self-driving vehicles linked with mobility electrification, system failures that occur suddenly in situations where customers are unaware of signs of failure are directly related to customer injuries. Securing the durability and safety of closure automation system is necessary to increase the customer's safety value so PHM technology makes it possible to predict failures and remaining life in advance during system operation. In addition, since not only various forms of new concept design styling but also innovative new handle designs are applied, it is obviously seen that e-Latch system is widely equipped in the mobility. Thus, in this paper, the study to predict the failure of e-Latch and closure system is implemented via data-driven and physics-driven method, and the algorithm for PHM to estimate remaining life of e-Latch system is also introduced.
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AI, CNN, Regession, Crash, Performance, Feedforward, Feature extraction, PHM, Prognostics and Health Management, RUL, Remaining, Useful Life, Machine Learning, 1D Simulation, Failure diagnostics, Failure classification, Median filter, Data preprocessing, Classification, Electric latch
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