The D-CNN-LSTM Autoencoder method optimizes the anomaly detection rate for all of the anomalies, specifically in the case of low magnitude anomalies, enhancing F1-score up to 18.12% in single types of ...

In this study the applicability of autoencoder-based deep neural networks to solve fault classification problems using vibration signals. This work will aim to construct CNN and LSTM type ...

The autoencoder consists of two parts, an encoder and a decoder, which encode the input into the embedding dimension and then output by the decoder to reconstructed the input from the embedding ...