End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis
Abstract
:1. Introduction
- Most current studies still need various signal processing methods to extract features. Therefore, the effectiveness of fault diagnosis heavily depends on the quality of manually extracted features. A suitable intelligent diagnosis algorithm is needed for adaptive feature extraction and selection.
- Some deep learning algorithms still need to cooperate with many complex signal processing methods to adapt to rolling bearing fault detection. These methods have a lot of manual parameters to adjust and are difficult to deploy and train.
- Some deep learning models improve the diagnosis effect by combining overly complex structures, but this usually increases the cost of calculation and the risk of overfitting.
- An end-to-end deep learning model is proposed for rolling bearing fault diagnosis. Without manual design features or complex data processing, this model can accurately extract and screen continuous/discontinuous signal features to diagnose rolling bearing faults.
- Compared with the simple deep learning model, the proposed model has a higher classification accuracy (99.87%), and the inference time does not significantly increase.
- This method could be easily deployed and migrated to a new environment or a new type of rolling bearing because of the absence of complex data processing and manual feature engineering.
- The proposed model can achieve accurate multitype fault diagnosis, and the experiment proved that it could accurately diagnose 10 types of working states of rolling bearings.
2. Materials and Methods
2.1. Types of Rolling Bearing Faults
2.2. Framework of the Proposed C/D-FUSA
2.3. Subnet for Continuous Features
2.3.1. Long Short-Term Memory
2.3.2. Context-Dependent Attention
2.4. Subnet for Discontinuous Features
2.5. Subnet for Classification
3. Experiments and Results
3.1. Experiment Setups
- Normal type: no fault was found in these samples;
- Location = Ball, Diameter = 0.007: the fault occurred on the ball, the fault diameter was 0.007 in;
- Location = Ball, Diameter = 0.014: the fault occurred on the ball, the fault diameter was 0.014 in;
- Location = Ball, Diameter = 0.021: the fault occurred on the ball, the fault diameter was 0.021 in;
- Location = Inner Raceway, Diameter = 0.007: the fault occurred on the inner raceway, the fault diameter was 0.007 in;
- Location = Inner Raceway, Diameter = 0.014: the fault occurred on the inner raceway, the fault diameter was 0.014 in;
- Location = Inner Raceway, Diameter = 0.021: the fault occurred on the inner raceway, the fault diameter was 0.021 in;
- Location = Outer Raceway, Diameter = 0.007: the fault occurred on the outer raceway, the fault diameter was 0.007 in;
- Location = Outer Raceway, Diameter = 0.014: the fault occurred on the outer raceway, the fault diameter was 0.014 in;
- Location = Outer Raceway, Diameter = 0.021: the fault occurred on the outer raceway, the fault diameter was 0.021 in.
3.2. Results
3.2.1. Performance Comparison of Different Models
3.2.2. Performance Comparison with Other Studies
3.2.3. Inference Time Comparison with Other Studies
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Fault Location | Diameter | Number of Samples | Number of Samples in CWRU-512 | Number of Samples in CWRU-6000 |
---|---|---|---|---|
Normal | Normal | 2,182,450 | 4261 | 360 |
Ball | 0.007 | 487,093 | 950 | 80 |
0.014 | 488,109 | 951 | 80 | |
0.021 | 487,964 | 951 | 80 | |
Inner Raceway | 0.007 | 488,309 | 952 | 80 |
0.014 | 487,239 | 948 | 80 | |
0.021 | 487,529 | 950 | 80 | |
Outer Raceway | 0.007 | 1,465,051 | 2855 | 240 |
0.014 | 487,819 | 950 | 80 | |
0.021 | 1,465,487 | 2856 | 240 |
Data Set | Hyper Parameter | Value |
---|---|---|
CWRU-512 | Learning Rate | 0.002 |
Epoch | 200 | |
Max length | 512 | |
Optimizer | Adaptive Moment Estimation (Adam) [46] | |
Loss Function | Cross-entropy loss | |
CWRU-6000 | Learning Rate | 0.001 |
Epoch | 200 | |
Max length | 6000 | |
Optimizer | Adam | |
Loss Function | Cross-entropy loss |
Data Set | Model | Accuracy (%) | Precision (%) | Recall (%) | F1 Score (%) |
---|---|---|---|---|---|
CRWU-512 | C/D-FUSA | 99.85 | 99.84 | 99.90 | 99.87 |
C/D-FUS | 99.64 | 99.60 | 99.65 | 99.62 | |
LSTM | 99.58 | 99.51 | 99.58 | 99.54 | |
CRWU-6000 | C/D-FUSA | 99.69 | 99.65 | 99.72 | 99.68 |
C/D-FUS | 99.50 | 99.52 | 99.51 | 99.51 | |
LSTM | 68.75 | 66.67 | 71.69 | 69.09 |
Method | Type | Number of Fault Classes | Number of Training Samples | Accuracy (%) |
---|---|---|---|---|
Sohaib et al., (2017) [33] | Hybrid features + sparse stacked autoencoder | 10 | 710 | 99.10 |
Li et al., (2019) [34] | Preprocessing + attention + LSTM + CNN | 10 | - | 99.74 |
Lei et al., (2016) [47] | Signal fraction + deep learning | 10 | 20,000 | 99.66 |
Wang et al., (2022) [48] | SSAE and softmax classifier | 10 | 4163 | 99.15 |
Yan et al., (2022) [49] | Markov transition field and residual network | 10 | 6600 | 98.52 |
Zhang et al., (2022) [50] | Transfer learning | 10 | 9518 | 99.80 |
Zhao et al., (2021) [51] | Adaptation network with adversarial learning | 10 | 7000 | 99.24 |
C/D-FUSA | Attention + LSTM + CNN | 10 | 13,299 | 99.85 |
Data Set | Model | Inference Time (s) |
---|---|---|
CRWU-512 | C/D-FUSA | 0.034 |
C/D-FUS | 0.031 | |
LSTM | 0.028 | |
CRWU-6000 | C/D-FUSA | 0.228 |
C/D-FUS | 0.220 | |
LSTM | 0.208 |
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Zheng, J.; Liao, J.; Chen, Z. End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis. Sensors 2022, 22, 6489. https://doi.org/10.3390/s22176489
Zheng J, Liao J, Chen Z. End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis. Sensors. 2022; 22(17):6489. https://doi.org/10.3390/s22176489
Chicago/Turabian StyleZheng, Jianbo, Jian Liao, and Zongbin Chen. 2022. "End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis" Sensors 22, no. 17: 6489. https://doi.org/10.3390/s22176489
APA StyleZheng, J., Liao, J., & Chen, Z. (2022). End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis. Sensors, 22(17), 6489. https://doi.org/10.3390/s22176489