Feb 25, 2019 ¡¤ This paper proposes an unsupervised method for diagnosing faults of electric motors by using a novelty detection approach based on deep autoencoders.

Fault diagnosis of electric motors is a fundamental task for production line testing, and it is usually performed by experienced human operators.

Feb 23, 2024 ¡¤ This paper proposes an unsupervised method for diagnosing faults of electric motors by using a novelty detection approach based on deep ...

Deep neural networks have been successfully employed for this task, but, up to the authors' knowledge, they have never been used in an unsupervised scenario.

An unsupervised method for diagnosing faults of electric motors by using a novelty detection approach based on deep autoencoders, and the results showed ...

The document proposes an unsupervised method for detecting faults in electric motors using deep autoencoders trained on vibration signal data from normal ...

The proposed method uses an autoencoder neural network for constructing a new motor vibration feature and a feed-forward neural network for the final detection.

3.2.3 Autoencoders. Autoencoders have been employed for unsupervised feature learning and dimensionality reduction in motor fault detection. They are ...

Jun 1, 2024 ¡¤ We develop a novel fault detection method that combines physics-based simulations for data generation with a Physics-Informed Deep Autoencoder (PIDAE)

This paper covers four traditional types of deep learning models: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), and ...