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.

One-Dimensional Convolutional Neural Networks for Real-Time Damage Detection of Rotating Machinery

2021 ◽  
pp. 73-83
Author(s):  
Onur Avci ◽  
Osama Abdeljaber ◽  
Serkan Kiranyaz ◽  
Sadok Sassi ◽  
Abdelrahman Ibrahim ◽  
...  
Keyword(s):  
Neural Networks ◽  
Damage Detection ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Rotating Machinery ◽  
One Dimensional

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 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.

scholarly journals Fault Diagnosis for Bearing Based on 1DCNN and LSTM

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Haibin Sun ◽  
Shichao Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Short Term Memory ◽  
Rotating Machinery ◽  
Fault Identification ◽  
Short Term ◽  
Term Memory ◽  
One Dimensional ◽  
Memory Network ◽  
Long Short Term Memory

Condition monitoring and fault diagnosis of the bearing are essential for the smooth operation of rotating machinery. In this paper, an end-to-end intelligent fault diagnosis method for bearing combining one-dimensional convolutional neural network with long short-term memory network (1DCNN-LSTM) is proposed for the deficiencies of existing fault diagnosis methods. First, the proposed method takes one-dimensional fault data directly as input. Second, one-dimensional convolutional neural network (1DCNN) is used for self-adaptively extracting robust features from the original bearing signal, and more features are extracted while ensuring the validity and saliency of the extracted features by combining maximum pooling and average pooling layers to downsample features. Then, long short-term memory network (LSTM) is used to learn the temporal dependencies among features. At last, fault identification is achieved. 1DCNN-LSTM does not require any manual feature extraction, and the errors caused by reliance on expert experience and incomplete information in traditional feature extraction methods are avoided. The results show that the proposed classifier with good generalization performance not only diagnoses the category of fault quickly and accurately under different load conditions but also achieves an average fault identification accuracy of 99.95%. For its powerful learning abilities, this method can also be applied to the bearing fault diagnosis of rotating machinery in many fields.

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