Research on deep autoencoder-based adaptive anomaly detection algorithm and its application
With the development of deep learning,deep autoencoder has been widely applied in anomaly detection with efficient data encoding and reconstruction mechanisms.However,some existing deep autoencoder-based anomaly detection algorithms still face many problems,such as complex and diverse data distributions,high false alarm rate,and high missing alarm rate,etc.To overcome the above-mentioned problems,we propose a deep autoencoder-based adaptive anomaly detection algorithm.The algorithm utilizes an adaptive landmark filtering mechanism via density peak,which can select some normal samples with high density as candidate landmarks,aiming to discover the diversity of normal samples in the latent feature space.Subsequently,the landmark filtering mechanism is employed to filter and optimize the candidate landmarks to enhance the representativeness and sparsity of the landmarks.Furthermore,we design a novel loss function to optimize the model parameters iteratively with the aim of enhancing the correlation between normal samples and landmarks.Finally,the proposed anomaly detection algorithm is applied to a battery fault diagnosis,and the experiment results demonstrate that this work outperforms the existing anomaly detection algorithms in terms of accuracy,false alarm rate,and missing alarm rate.It can identify faulty batteries effectively,and provide the technical support and precise services for the battery fault identification and state management.
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