Multisource Data Fusion Diagnosis Method of Rolling Bearings Based on Improved Multiscale CNN
Intelligent diagnosis applies deep learning algorithms to mechanical fault diagnosis, which can classify the fault forms of machines or parts efficiently. At present, the intelligent diagnosis of rolling bearings mostly adopts a single-sensor signal, and multisensor information can provide more comp...
Enregistré dans:
Auteurs principaux: | Yulin Jin, Changzheng Chen, Siyu Zhao |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Hindawi Limited
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/f69df71339634749b6ca32d123f45ba3 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Coastal Ecological Environment Monitoring and Protection System Based on Multisource Information Fusion Decision
par: Lijuan Xu, et autres
Publié: (2021) -
An Efficient Rolling Bearing Fault Diagnosis Method Based on Spark and Improved Random Forest Algorithm
par: Lanjun Wan, et autres
Publié: (2021) -
Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN
par: Jiajun He, et autres
Publié: (2021) -
A Novel Intelligent Fault Diagnosis Method for Rolling Bearings Based on Compressed Sensing and Stacked Multi-Granularity Convolution Denoising Auto-Encoder
par: Chuang Liang, et autres
Publié: (2021) -
Composite Fault Diagnosis of Rolling Bearing Based on Optimized Wavelet Packet AR Spectrum Energy Entropy Combined with Adaptive No Velocity Term PSO-SOM-BPNN
par: Hongwei Fan, et autres
Publié: (2021)