Parallel multi-fusion convolutional neural networks based fault diagnosis of rotating machinery under noisy environments

2021 ◽  
Author(s):  
Guoqiang Li ◽  
Jun Wu ◽  
Chao Deng ◽  
Zuoyi Chen
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Convolutional Neural Networks ◽  
Rotating Machinery ◽  
Noisy Environments

Rub-Impact Fault Diagnosis of Rotating Machinery Based on 1-D Convolutional Neural Networks

2020 ◽  
Vol 20 (15) ◽  
pp. 8349-8363 ◽  
Author(s):  
Xinya Wu ◽  
Zhike Peng ◽  
Jishun Ren ◽  
Changming Cheng ◽  
Wenming Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Convolutional Neural Networks ◽  
Rotating Machinery

scholarly journals A Fault Diagnosis Method of Rotating Machinery Based on One-Dimensional, Self-Normalizing Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3837 ◽  
Author(s):  
Jingli Yang ◽  
Shuangyan Yin ◽  
Yongqi Chang ◽  
Tianyu Gao
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Convolutional Neural Networks ◽  
Activation Function ◽  
Rotating Machinery ◽  
Identification Accuracy ◽  
Stability And Convergence ◽  
Fault Identification ◽  
One Dimensional ◽  
The Stability

Aiming at the fault diagnosis issue of rotating machinery, a novel method based on the deep learning theory is presented in this paper. By combining one-dimensional convolutional neural networks (1D-CNN) with self-normalizing neural networks (SNN), the proposed method can achieve high fault identification accuracy in a simple and compact architecture configuration. By taking advantage of the self-normalizing properties of the activation function SeLU, the stability and convergence of the fault diagnosis model are maintained. By introducing α -dropout mechanism twice to regularize the training process, the overfitting problem is resolved and the generalization capability of the model is further improved. The experimental results on the benchmark dataset show that the proposed method possesses high fault identification accuracy and excellent cross-load fault diagnosis capability.

scholarly journals A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 671
Author(s):  
Daoguang Yang ◽  
Hamid Reza Karimi ◽  
Len Gelman
Keyword(s):  
Neural Networks ◽  
Fourier Transform ◽  
Fault Diagnosis ◽  
Convolutional Neural Networks ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Deep Convolutional Neural Networks ◽  
Signal Features ◽  
Fuzzy Fusion ◽  
Machinery Fault Diagnosis

Some artificial intelligence algorithms have gained much attention in the rotating machinery fault diagnosis due to their robust nonlinear regression properties. In addition, existing deep learning algorithms are usually dependent on single signal features, which would lead to the loss of some information or incomplete use of the information in the signal. To address this problem, three kinds of popular signal processing methods, including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT) and directly slicing one-dimensional data into the two-dimensional matrix, are used to create four different datasets from raw vibration signal as the input data of four enhancement Convolutional Neural Networks (CNN) models. Then, a fuzzy fusion strategy is used to fuse the output of four CNN models that could analyze the importance of each classifier and explore the interaction index between each classifier, which is different from conventional fusion strategies. To show the performance of the proposed model, an artificial fault bearing dataset and a real-world bearing dataset are used to test the feature extraction capability of the model. The good anti-noise and interpretation characteristics of the proposed method are demonstrated as well.

Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks

2018 ◽  
Vol 23 (1) ◽  
pp. 101-110 ◽  
Author(s):  
Min Xia ◽  
Teng Li ◽  
Lin Xu ◽  
Lizhi Liu ◽  
Clarence W. de Silva
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Convolutional Neural Networks ◽  
Rotating Machinery ◽  
Multiple Sensors
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