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    Unsupervised Anomaly Detection for Power Batteries: A Temporal Convolution Autoencoder Framework 

    Source: Journal of Electrochemical Energy Conversion and Storage:;2024:;volume( 022 ):;issue: 001:;page 11009-1
    Author(s): Wang, Juan; Ye, Yonggang; Wu, Minghu; Zhang, Fan; Cao, Ye; Zhang, Zetao; Chen, Ming; Tang, Jing
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: To prevent potential abnormalities from escalating into critical faults, a rapid and precise algorithm should be employed for detecting power battery anomalies. An unsupervised model based on a temporal convolutional ...
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