Novelty Detection and Fault Diagnosis Method for Bearing Faults Based on the Hybrid Deep Autoencoder Network
Abstract
:1. Introduction
- (1)
- This paper designs a hybrid deep autoencoder network composed of the convolutional autoencoder, new-fault detector, and classifier. It achieves the functionality of detecting novel faults and classifying known faults. By setting a threshold based on reconstruction errors, the proposed method automatically determines whether a fault belongs to unknown faults, thereby addressing the issue of misclassifying unknown faults as known faults.
- (2)
- This method employs unsupervised training of the hybrid deep autoencoder network using data from known fault classes to obtain reconstruction data and low-dimensional features of the samples. Additionally, it utilizes a small amount of labeled data for supervised training of the network. This approach accelerates the training speed of the network and reduces the required sample size for training.
- (3)
- Through comparisons with LSTM and SAE models, in terms of novel fault recognition performance and fault classification, it is demonstrated that the proposed model performs well in both novel fault detection and known fault classification. Experimental results show that the overall detection performance of the hybrid deep autoencoder network model is superior to other models, with higher detection results on all three datasets. This confirms the effectiveness of the proposed method.
2. Related Work
3. Novelty Detection and Fault Diagnosis Method Based on a Hybrid Deep Autoencoder Network
3.1. Network Structure
3.1.1. Encoder
3.1.2. Decoder
3.1.3. Detector
3.1.4. Classifier
3.2. Loss Function
3.3. Diagnostic Process
4. Experiment
4.1. Dataset
4.1.1. CWRU Dataset
4.1.2. The Paderborn Dataset
4.1.3. MFPT Dataset
4.2. Implementation
4.3. Evaluation Indicators
4.4. Experimental Results and Analysis
4.4.1. Novelty Detection Performance
4.4.2. Fault Classification Performance
4.4.3. Model Complexity Analysis
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Component | Layer Types | Output Size | Other Parameter Configurations |
---|---|---|---|
Encoder | Input layer | (1, 64, 64) | |
Convolutional layer | (16, 32, 32) | kernel = 3 × 3, stride = 2, padding = 1 | |
Convolutional layer | (32, 16, 16) | kernel = 3× 3, stride = 2, padding = 1 | |
Batch Normalization layer | (32, 16, 16) | ||
Convolutional layer | (32, 8, 8) | kernel = 3 × 3, stride = 2, padding = 1 | |
Convolutional layer | (32, 4, 4) | kernel = 3 × 3, stride = 2, padding = 1 | |
Flatten layer | (512) | ||
Fully connected layer | (64) | ||
Fully connected layer | (32) | ||
Decoder | Fully connected layer | (64) | |
Flatten layer | (512) | ||
Unflattened layer | (32, 4, 4) | ||
Deconvolutional layer | (32, 8, 8) | kernel = 3 × 3, stride = 2, padding = 1 | |
Deconvolutional layer | (32, 16, 16) | kernel = 3 × 3, stride = 2, padding = 1 | |
Batch Normalization layer | (32, 16, 16) | ||
Deconvolutional layer | (16, 32, 32) | kernel = 3 × 3, stride = 2, padding = 1 | |
Deconvolutional layer | (16, 32, 32) | kernel = 3 × 3, stride = 2, padding = 1 | |
Classifier | Softmax layer | (C) |
Fault Class | Fault Diameter (inch) | Class Label | Number of Original Data Points | Number of Samples |
---|---|---|---|---|
Normal | / | C1 | 243,938 | 238 |
Inner race fault | 0.007 | C2 | 121,265 | 118 |
Inner race fault | 0.014 | C3 | 121,846 | 118 |
Inner race fault | 0.021 | C4 | 122,136 | 119 |
Balls fault | 0.007 | C5 | 122,571 | 119 |
Balls fault | 0.014 | C6 | 121,846 | 118 |
Balls fault | 0.021 | C7 | 121,991 | 119 |
Outer race fault (at 6 o ’clock) | 0.007 | C8 | 121,991 | 119 |
Outer race fault (at 6 o ’clock) | 0.014 | C9 | 121,846 | 118 |
Outer race fault (at 6 o ’clock) | 0.021 | C10 | 122,426 | 119 |
File Number | Location of Damage | Class | Damage Mode | Mode of Distribution | Degree of Damage |
---|---|---|---|---|---|
K005 | / | C1 | / | / | / |
KI04 | Inner | C2 | multiple | no repetition | 1 |
KI14 | Inner | C3 | multiple | no repetition | 1 |
KI16 | Inner | C4 | single | no repetition | 3 |
KI17 | Inner | C5 | single | random | 1 |
KA04 | Outer | C6 | single | no repetition | 1 |
KA16 | Outer | C7 | repetitive | random | 2 |
Type of Fault | Class | Sampling Rate (sps) | Load (Ibs) | Number of Data Points | Sample Size |
---|---|---|---|---|---|
normal | C1 | 97,656 | 270 | 58,5936 | 572 |
Outer | C2 | 97,656 | 270 | 58,5936 | 572 |
Outer | C3 | 48,828 | 25 | 146,484 | 143 |
Outer | C4 | 48,828 | 50 | 146,484 | 143 |
Outer | C5 | 48,828 | 100 | 146,484 | 143 |
Inner | C6 | 48,828 | 0 | 146,484 | 143 |
Inner | C7 | 48,828 | 50 | 146,484 | 143 |
Inner | C8 | 48,828 | 100 | 146,484 | 143 |
TPR | TNR | F1 | |
---|---|---|---|
Ours | 98.98% | 98.59% | 0.98 |
LSTM | 98.86% | 60.35% | 0.75 |
SAE | 88.57% | 98.53% | 0.93 |
TPR | TNR | F1 | |
---|---|---|---|
Ours | 92.83% | 96.79% | 0.94 |
LSTM | 80.53% | 56.24% | 0.63 |
SAE | 87.00% | 96.99% | 0.91 |
TPR | TNR | F1 | |
---|---|---|---|
Ours | 98.31% | 84.34% | 0.92 |
LSTM | 94.27% | 53.57% | 0.71 |
SAE | 88.52% | 92.35% | 0.89 |
CWRU | Paderborn | MFPT | |
---|---|---|---|
Ours | 100% | 98.53% | 97.03% |
LSTM | 79.85% | 79.35% | 72.89% |
SAE | 100% | 97.98% | 96.73% |
Total Params | Training Time (s) | |
---|---|---|
Ours | 117,224 | 203 |
LSTM | 1,084,544 | 1272 |
SAE | 1,678,629 | 869 |
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Share and Cite
Zhao, Y.; Hao, H.; Chen, Y.; Zhang, Y. Novelty Detection and Fault Diagnosis Method for Bearing Faults Based on the Hybrid Deep Autoencoder Network. Electronics 2023, 12, 2826. https://doi.org/10.3390/electronics12132826
Zhao Y, Hao H, Chen Y, Zhang Y. Novelty Detection and Fault Diagnosis Method for Bearing Faults Based on the Hybrid Deep Autoencoder Network. Electronics. 2023; 12(13):2826. https://doi.org/10.3390/electronics12132826
Chicago/Turabian StyleZhao, Yuanyuan, Huijuan Hao, Yu Chen, and Yu Zhang. 2023. "Novelty Detection and Fault Diagnosis Method for Bearing Faults Based on the Hybrid Deep Autoencoder Network" Electronics 12, no. 13: 2826. https://doi.org/10.3390/electronics12132826
APA StyleZhao, Y., Hao, H., Chen, Y., & Zhang, Y. (2023). Novelty Detection and Fault Diagnosis Method for Bearing Faults Based on the Hybrid Deep Autoencoder Network. Electronics, 12(13), 2826. https://doi.org/10.3390/electronics12132826