A Fault Detection Framework for Rotating Machinery with a Spectrogram and Convolutional Autoencoder
In modern industrial systems, establishing the optimal maintenance policy for rotating machinery is essential to improve productivity and prevent catastrophic accidents. To this end, many machinery engineers have been interested in condition-based maintenance strategies, which execute the maintenanc...
Saved in:
| Main Authors: | Hoyeon Lee, Jaehong Yu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/14/7698 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Identification of temporal anomalies of spectrograms of vibration measurements of a turbine generator rotor using a recurrent neural network autoencoder
by: V. P. Kulagin, et al.
Published: (2021-04-01) -
Detection of Abnormal Symptoms Using Acoustic-Spectrogram-Based Deep Learning
by: Seong-Yoon Kim, et al.
Published: (2025-04-01) -
Research Status and Prospect of Torsional Vibration Detection Methods for Rotating Machinery
by: GUO Yan-ling, et al.
Published: (2021-12-01) -
Abnormal sound detection method for coal mine belt conveyors based on convolutional autoencoder
by: SHEN Long, et al.
Published: (2025-02-01) -
Source-Free Domain Adaptation Framework for Rotary Machine Fault Diagnosis
by: Hoejun Jeong, et al.
Published: (2025-07-01)