A Two-Stage Semi-Supervised Learning Framework for Fault Diagnosis of Rotating Machinery

2021 ◽  
pp. 1-1
Author(s):  
Xiaoyin Nie ◽  
Gang Xie
Keyword(s):  
Fault Diagnosis ◽  
Supervised Learning ◽  
Rotating Machinery ◽  
Two Stage ◽  
Learning Framework

Self-supervised Learning Advance Fault Diagnosis of Rotating Machinery

2021 ◽  
pp. 319-332
Author(s):  
Baoming Zhang ◽  
Yongfang Mao ◽  
Xin Chen ◽  
Yi Chai ◽  
Zhenli Yang
Keyword(s):  
Fault Diagnosis ◽  
Supervised Learning ◽  
Rotating Machinery

scholarly journals Software design of rotating machinery fault diagnosis system based on deep learning

2021 ◽  
Vol 260 ◽  
pp. 03006
Author(s):  
Xiaofeng He ◽  
Xiaofeng Liu ◽  
Xiulian Lu ◽  
Lipeng He ◽  
Yunxiang Ma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Operating Conditions ◽  
Diagnosis System ◽  
Training Requirements ◽  
Learning Framework ◽  
Software Interface ◽  
Python Language ◽  
Fault Diagnosis System

With the development of Industry 4.0, in order to meet the needs of intelligent fault diagnosis of rotating machinery in the industrial field, this paper developed a fault diagnosis system for rotating machinery based on deep learning and wavelet transform methods. The system is based on the Python language and mainly combines the PyQt graphical interface framework and the TensorFlow machine learning framework to complete the training requirements for historical or online fault data, and perform online monitoring and diagnosis of equipment operating conditions. The diagnostic accuracy of the system test results is more than 95%, the software interface is friendly, the algorithm generalization ability is good, and the reliability is strong. It provides guidance for the diagnosis of rotating machinery.

Construction of a deep sparse filtering network for rotating machinery fault diagnosis

2021 ◽  
pp. 095440702110148
Author(s):  
Chun Cheng ◽  
Wei Zou ◽  
Weiping Wang ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Back Propagation ◽  
Feature Learning ◽  
Back Propagation Neural Network ◽  
Rotating Machinery ◽  
Fine Tuning ◽  
Local Optimum ◽  
Back Propagation Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Sparse Filtering

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

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