A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON SDP AND IMPROVED SAM⁃MobileNetv2
Traditional fault diagnosis methods for rolling bearings are difficult to accurately and efficiently achieve fault classification.A method of rolling bearing fault classification based on symmetrized dot pattern(SDP)and improved SAM⁃MobileNetv2 was proposed.Firstly,the bearing vibration signal was transformed into two⁃dimensional images with rich characteristic information by SDP algorithm.Secondly,the two⁃dimensional images were fed into the SAM⁃MobileNetv2 network model,which extracted and classified fault feature information.Improved SAM⁃MobileNetv2 networks used the adaptive activation function ACON to replace the ReLU6 activation function in SAM⁃MobileNetv2 to improve model classification performance.Finally,this model was compared with various models.The experimental results show that this model can accurately and efficiently realize the classification of rolling bearing faults,using Case Western Reserve University bearing fault data with an accuracy rate of 99.5%,using the University of Ottawa bearing failure data with an accuracy rate of 97.2%.
FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON SDP AND IMPROVED SAM⁃MobileNetv2
Traditional fault diagnosis methods for rolling bearings are difficult to accurately and efficiently achieve fault classification.A method of rolling bearing fault classification based on symmetrized dot pattern(SDP)and improved SAM⁃MobileNetv2 was proposed.Firstly,the bearing vibration signal was transformed into two⁃dimensional images with rich characteristic information by SDP algorithm.Secondly,the two⁃dimensional images were fed into the SAM⁃MobileNetv2 network model,which extracted and classified fault feature information.Improved SAM⁃MobileNetv2 networks used the adaptive activation function ACON to replace the ReLU6 activation function in SAM⁃MobileNetv2 to improve model classification performance.Finally,this model was compared with various models.The experimental results show that this model can accurately and efficiently realize the classification of rolling bearing faults,using Case Western Reserve University bearing fault data with an accuracy rate of 99.5%,using the University of Ottawa bearing failure data with an accuracy rate of 97.2%.
FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON SDP AND IMPROVED SAM⁃MobileNetv2
ZHANG TianYuan (author) / SUN HuEr (author) / ZHU JiYang (author) / ZHAO Yang (author)
2024
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Fault Diagnosis of Rolling Bearings Based on LLE_KFDA
| British Library Online Contents | 2009
FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON CHANNEL AND SPATIAL RECONSTRUCTION NETWORKS
| DOAJ | 2024
Fault Diagnosis of Rolling Bearings Based on Wavelet Packet and Spectral Kurtosis
| Tema Archive | 2011
FAULT DIAGNOSIS METHOD OF ROLLING BEARINGS BASED ON ELMD AND KERNEL DENSITY ESTIMATION
| DOAJ | 2017
APPLICATION OF CONVOLUTIONAL NEURAL NETWORK AND CHAOS THEORY IN FAULT DIAGNOSIS OF ROLLING BEARINGS
| DOAJ | 2022