scholarly journals Smart Fault Diagnostics using Convolutional Neural Network and Adam Stochastic Optimization

2021 ◽  
Vol 3 (1) ◽  
pp. 38-46
Author(s):  
Subarna Shakya
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Nonlinear Problems ◽  
Basic Structure ◽  
Rotating Machinery ◽  
Intelligent Fault Diagnosis ◽  
Performance Diagnosis ◽  
Large Scope ◽  
And Performance

Navigation, aviation and several other fields of engineering extensively make use of rotating machinery. The stability and safety of the equipment as well as the personnel are affected by this machinery. Use of deep learning as the basis of intelligent fault diagnosis schemes has and investigation of other relevant fault diagnosis schemes has a large scope for development. Thorough exploration needs to be performed in deep neural network (DNN) based schemes as shallow layer network structure based fault diagnosis schemes that are currently available has several considerable limitations. The nonlinear problems may be processed during intelligent fault diagnosis using deep convolutional neural network, which is a special structure DNN. The convolutional neural network (CNN) based scheme is emphasized in this paper. The principle and basic structure of the model are introduced. In rotating machinery, the fault diagnosis schemes using CNN are analyzed and summarized. Various CNN schemes, the potential mechanisms and performance diagnosis are analyzed. A novel smart fault diagnosis strategy is proposed while highlighting the potential aspects of existing schemes and reviewing the challenges.

scholarly journals A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1693 ◽  
Author(s):  
Gong ◽  
Chen ◽  
Zhang ◽  
Zhang ◽  
Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Back Propagation ◽  
Rotating Machinery ◽  
Fault Classification ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
K Nearest Neighbor

Intelligent fault diagnosis methods based on deep learning becomes a research hotspot in the fault diagnosis field. Automatically and accurately identifying the incipient micro-fault of rotating machinery, especially for fault orientations and severity degree, is still a major challenge in the field of intelligent fault diagnosis. The traditional fault diagnosis methods rely on the manual feature extraction of engineers with prior knowledge. To effectively identify an incipient fault in rotating machinery, this paper proposes a novel method, namely improved the convolutional neural network-support vector machine (CNN-SVM) method. This method improves the traditional convolutional neural network (CNN) model structure by introducing the global average pooling technology and SVM. Firstly, the temporal and spatial multichannel raw data from multiple sensors is directly input into the improved CNN-Softmax model for the training of the CNN model. Secondly, the improved CNN are used for extracting representative features from the raw fault data. Finally, the extracted sparse representative feature vectors are input into SVM for fault classification. The proposed method is applied to the diagnosis multichannel vibration signal monitoring data of a rolling bearing. The results confirm that the proposed method is more effective than other existing intelligence diagnosis methods including SVM, K-nearest neighbor, back-propagation neural network, deep BP neural network, and traditional CNN.

scholarly journals Rotating machinery fault diagnosis based on a novel lightweight convolutional neural network

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256287
Author(s):  
Jing Yan ◽  
Tingliang Liu ◽  
Xinyu Ye ◽  
Qianzhen Jing ◽  
Yuannan Dai
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Rotating Machinery ◽  
Model Parameters ◽  
Storage Space ◽  
Intelligent Fault Diagnosis ◽  
Industrial Internet ◽  
T Distribution ◽  
And Storage

The advancement of Industry 4.0 and Industrial Internet of Things (IIoT) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis. In this case, this paper proposes a lightweight convolutional neural network (LCNN) method for intelligent fault diagnosis of rotating machinery, which can largely satisfy the need of less parameter amount and storage space as well as high accuracy. First, light-weight convolution blocks are constructed through basic elements such as spatial separable convolutions with the aim to effectively reduce model parameters. Secondly, the LCNN model for the intelligent fault diagnosis is constructed via lightweight convolution blocks instead of the tradi-tional convolution operation. Finally, to address the “black box” problem, the entire network is visualized through Tensorboard and t-distribution stochastic neighbor embedding. The results demonstrate that when the number of lightweight convolutional blocks reaches 6, the diagnosis accuracy of the LCNN model exceeds 99.9%. And the proposed model has become the most robust with parameters significantly decreasing. Furthermore, the proposed LCNN model has realized accurate, automatic, and robust fault diagnosis of rotating machinery, which makes it more suitable for deployment under the IIoT context.

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