A Bearing Fault Diagnosis Method Based on L1 Regularization Transfer Learning and LSTM Deep Learning

2021 ◽  
Author(s):  
Daiie Zhu ◽  
Xudong Song ◽  
Jie Yang ◽  
Yuyang Cong ◽  
Lijuan Wang
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Transfer Learning ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
L1 Regularization ◽  
Diagnosis Method

scholarly journals Multi-view Bearing Fault Diagnosis Method Based on Deep Learning

2021 ◽  
Vol 1757 (1) ◽  
pp. 012093
Author(s):  
Ruihan Wang ◽  
Xinyu Meng ◽  
Bangru Xiong ◽  
Zhengxia Wang
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

scholarly journals Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1088 ◽  
Author(s):  
Gaowei Xu ◽  
Min Liu ◽  
Zhuofu Jiang ◽  
Dirk Söffker ◽  
Weiming Shen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Ensemble Learning ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Multi Level

Recently, research on data-driven bearing fault diagnosis methods has attracted increasing attention due to the availability of massive condition monitoring data. However, most existing methods still have difficulties in learning representative features from the raw data. In addition, they assume that the feature distribution of training data in source domain is the same as that of testing data in target domain, which is invalid in many real-world bearing fault diagnosis problems. Since deep learning has the automatic feature extraction ability and ensemble learning can improve the accuracy and generalization performance of classifiers, this paper proposes a novel bearing fault diagnosis method based on deep convolutional neural network (CNN) and random forest (RF) ensemble learning. Firstly, time domain vibration signals are converted into two dimensional (2D) gray-scale images containing abundant fault information by continuous wavelet transform (CWT). Secondly, a CNN model based on LeNet-5 is built to automatically extract multi-level features that are sensitive to the detection of faults from the images. Finally, the multi-level features containing both local and global information are utilized to diagnose bearing faults by the ensemble of multiple RF classifiers. In particular, low-level features containing local characteristics and accurate details in the hidden layers are combined to improve the diagnostic performance. The effectiveness of the proposed method is validated by two sets of bearing data collected from reliance electric motor and rolling mill, respectively. The experimental results indicate that the proposed method achieves high accuracy in bearing fault diagnosis under complex operational conditions and is superior to traditional methods and standard deep learning methods.

scholarly journals Rolling bearing fault diagnosis method based on VMD and LSSVM

2021 ◽  
Vol 1792 (1) ◽  
pp. 012035
Author(s):  
Xingtong Zhu ◽  
Zhiling Huang ◽  
Jinfeng Chen ◽  
Junhao Lu
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

Bearing Fault Diagnosis with Deep Learning Models

2020 ◽  
Author(s):  
Chia-An Yi ◽  
Yu-Ling Wang ◽  
Huei-Yang Lai ◽  
Yi-Wei Chen ◽  
Chan-Yun Yang
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Learning Models ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis
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