scholarly journals Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

Sensors ◽  
2016 ◽  
Vol 16 (6) ◽  
pp. 895 ◽  
Author(s):  
Chuan Li ◽  
René-Vinicio Sánchez ◽  
Grover Zurita ◽  
Mariela Cerrada ◽  
Diego Cabrera
Keyword(s):  
Fault Diagnosis ◽  
Feature Learning ◽  
Rotating Machinery ◽  
Vibration Measurement ◽  
Statistical Feature

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.

A novel intelligent fault diagnosis method based on variant sparse filtering and back-propagation

2021 ◽  
pp. 095745652110557
Author(s):  
Lifeng Chan ◽  
Chun Cheng
Keyword(s):  
Fault Diagnosis ◽  
Back Propagation ◽  
Feature Learning ◽  
Rotating Machinery ◽  
Learning Ability ◽  
Back Propagation Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Propagation Algorithm ◽  
Sparse Filtering ◽  
Diagnosis Method

Detecting the mechanical faults of rotating machinery in time plays a key role in avoiding accidents. With the coming of the big data era, intelligent fault diagnosis methods based on machine learning models have become promising tools. To improve the feature learning ability, an unsupervised sparse feature learning method called variant sparse filtering is developed. Then, a fault diagnosis method combining variant sparse filtering with a back-propagation algorithm is presented. The involvement of the back-propagation algorithm can further optimize the weight matrix of variant sparse filtering using label data. At last, the developed diagnosis method is validated by rolling bearing and planetary gearbox experiments. The experiment results indicate that the developed method can achieve high accuracy and good stability in rotating machinery fault diagnosis.

Adaptive deep feature learning network with Nesterov momentum and its application to rotating machinery fault diagnosis

2018 ◽  
Vol 305 ◽  
pp. 1-14 ◽  
Author(s):  
Shenghao Tang ◽  
Changqing Shen ◽  
Dong Wang ◽  
Shuang Li ◽  
Weiguo Huang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Feature Learning ◽  
Rotating Machinery ◽  
Learning Network ◽  
Deep Feature ◽  
Deep Feature Learning ◽  
Machinery Fault Diagnosis

scholarly journals A Rotating Machinery Fault Diagnosis Method Based on Feature Learning of Thermal Images

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 12348-12359 ◽  
Author(s):  
Zhen Jia ◽  
Zhenbao Liu ◽  
Chi-Man Vong ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Feature Learning ◽  
Rotating Machinery ◽  
Thermal Images ◽  
Diagnosis Method ◽  
Machinery Fault Diagnosis

Imbalanced data fault diagnosis of rotating machinery using synthetic oversampling and feature learning

2018 ◽  
Vol 48 ◽  
pp. 34-50 ◽  
Author(s):  
Yuyan Zhang ◽  
Xinyu Li ◽  
Liang Gao ◽  
Lihui Wang ◽  
Long Wen
Keyword(s):  
Fault Diagnosis ◽  
Imbalanced Data ◽  
Feature Learning ◽  
Rotating Machinery
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