Mar 1, 2020 · A new DNN model, one-dimensional convolutional auto-encoder (1D-CAE) is proposed for fault detection and diagnosis of multivariate processes in this paper. 1D-CAE is utilized to learn hierarchical feature representations through noise reduction of high-dimensional process signals.

This study proposes Sliding Window One-Dimensional Convolutional Autoencoder (SW1DCAE), an unsupervised vibration anomaly detection algorithm for industrial robots, that can directly act on the original vibration signal and effectively improve detection accuracy.

Jan 13, 2020 · In this article, a new DNN, one-dimensional residual convolutional autoencoder (1-DRCAE), is proposed for learning features from vibration signals directly in an unsupervised-learning way.

Apr 27, 2025 · Convolutional Neural Networks (CNNs) are well-known for their ability to process images by transforming a two-dimensional image into a compact, one-dimensional vector that captures the essential features. But what if we could reverse this process?

Sep 9, 2021 · This paper proposes a new DNN model, a one-dimension residual convolutional auto-encoder (1DRCAE), where unsupervised learning is used to extract representative features from complex industrial processes. 1DRCAE effectively integrates the one-dimensional convolutional kernel with an auto-encoder and is embedded residual learning block for ...

Dec 25, 2022 · To deal with this problem, this paper proposes a novel unsupervised fault detection method named one-dimension convolutional adversarial autoencoder (1DAAE), which introduces two new ideas: one-dimension convolution layers for the encoder to obtain better features and the adversarial thought to impose the latent variable z to cluster into a ...

Feb 24, 2024 · Autoencoders are a specific type of feedforward neural networks where the input is the same as the output. They compress the input into a lower-dimensional latent representation and then...

Aug 15, 2024 · We apply convolutional autoencoder to knowledge tracing field, using one-dimensional residual convolutional autoencoder to extract students’ knowledge state by retrieving useful exercise information in encoder path and then …

Mar 1, 2024 · In this study, we propose a novel method called one-dimensional deep convolutional autoencoder active infrared thermography (1D-DCAE-AIRT) aimed at enhancing the visualization of internal defects within FRP composites.

Dec 30, 2021 · The work applied a one-dimensional convolutional layer to the encoder of the first half of the autoencoder model and designed a convolutional autoencoder-based network intrusion detection model to reduce the dependence of model training on abnormal data and increase the recall rate of abnormal data detection.