Meta-Learning-in-Fault-Diagnosis

The source codes for Meta-learning in few-shot cross-domain fault diagnosis. Based on this work, our article Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects has been published.

1. Instructions

• To run all models, the requirements of your python environmrnt are as: 1) pytorch 1.8.1+cu102; 2) tensorflow-gpu 2.4.0. Note that only MANN is implemented by tensorflow, all other methods are achieved by pytorch. Thus, with pytorch only, you can observe the performance of most methods on CWRU dataset.
• Some packages you have to install: 1) tensorflow_addons (for optimizer AdamW in tensorflow. Not really necessary); 2) learn2learn. The latter is an advanced API to achieve meta-learning methods, which is definitely compatible with pytorch. If you have problems when installing learn2learn, such as 'Microsoft Visual C++ 14.0 is required.', please refer to this blog. Also, You can refer to this blog for quick start ; 3) Visdom (for visualization).
• Note that the learn2learn version we used is 0.1.5, if you have issues when using learn2learn, you can use this version or a higher version (>=0.1.5). Or you can modify the codes as #1
• Change the data path in cwru_path.py to put your own root_dir of CWdata_12k.
• The codes of these methods follow the idea of the original paper as far as possible, of course, for application in fault diagnosis, there are some modifications.

2. Methods

1. CNN
2. CNN with fine-tuning (CNN-FT) [1]
3. CNN with Maximum Mean Discrepancy (CNN-MMD) [2]
4. Model Agnostic Meta-Learning (MAML) [3]
5. Reptile [4]
6. Memory Augmented Neural Network (MANN) [5]
7. Prototypical Networks (ProtoNet) [6]
8. Relation Networks (RelationNet) [7]

NOTE: You can get weights of all well-Trained models now.

• Google Drive 地址：https://drive.google.com/drive/folders/1leHVoYXpMVXM_e148KmBWVaX0WkTZfO6?usp=sharing
• BaiduNetDisk 链接：https://pan.baidu.com/s/1tyj3B7CuM9Tu1-WcizAAbQ 提取码：oe9p

2.1 Feature extractor

The backbone of these methods, i.e. feature extractor, consists of four convolution blocks, as follows

import torch.nn as nn

def conv_block(in_channels, out_channels):
    return nn.Sequential(
        nn.Conv1d(in_channels, out_channels, kernel_size=3, padding=1),
        nn.BatchNorm1d(out_channels),
        nn.ReLU(),
        nn.MaxPool1d(kernel_size=2),
    )


class encoder_net(nn.Module):
    def __init__(self, in_chn, hidden_chn, cb_num=4):
        super().__init__()
        conv1 = conv_block(in_chn, hidden_chn)
        conv1_more = [conv_block(hidden_chn, hidden_chn) for _ in range(cb_num - 1)]
        self.feature_net = nn.Sequential(conv1, *conv1_more)  # (None, 64, 1024/2^4)

    def forward(self, x):
        return self.feature_net(x)

2.2 Tasks on CWRU bearing dataset

T1: 10 ways, load 3 ==> 10 ways, load 0  
T2: 6 ways, load 0 ==> 4 ways, load 0  
Details can be found in `cwru_path.py` 

Tasks	Source categories	Target categories	Source load	Target load
T1	{NC, IF1, IF2, IF3, ..., RoF3 }	{NC, IF1, IF2, IF3, ..., RoF3}	3	0
T2	{IF1, IF2, IF3, OF1, OF2, OF3}	{NC, RoF1, RoF2, RoF3 }	0	0

2.3 Results (Click on the image to see details)

Fig. 1. Results on T1.	Fig. 2. Results on T2.	Fig. 3. Test time and model memory.

2.4 Result Details

CNN-based methods

• CNN

Tasks	shots	Acc.(%)	Test time (s)	Trainging time (s)	Memory (KB)
T1	5	71.80	1.183	2.484	321

• CNN-FT

Tasks	shots	Acc.(%)	Test time (s)	Trainging time (s)	Memory (KB)
T1	5	75.90	3.995	2.484	321
T1	1	48.00	3.45	-	321
T2	5	82.50	5.72	-	225
T2	1	68.00	4.68	-	225

• CNN-MMD

Tasks	shots	Acc.(%)	Test time (s)	Trainging time (s)	Memory (KB)
T1	5	81.35	1.164	15.38	321

Meta-learning methods

• MAML

Tasks	shots	Acc.(%)	Test time (s)	Trainging time (s)	Memory (KB)
T1	5	95.80	5.654	720	321
T1	1	87.40	4.494	233	321
T2	5	91.95	6.507	312	225
T2	1	77.50	4.455	340	225

• Reptile

Tasks	shots	Acc.(%)	Test time (s)	Trainging time (s)	Memory (KB)
T1	5	94.6	12.04	1820	321
T1	1	bad	-	-	-
T2	5	91.50	17.528	585.6	225
T2	1	55.15	17.59	532	225

• ProtoNet

Tasks	shots	Acc.(%)	Test time (s)	Trainging time (s)	Memory (KB)
T1	5	95.30	0.290	41	160
T1	1	87.69	0.121	24	160
T2	5	89.18	0.161	-	160
T2	1	77.25	0.104	-	160

• RelationNet

Tasks	shots	Acc.(%)	Test time (s)	Trainging time (s)	Memory (KB)
T1	5	92.34	0.3472	304	1339
T1	1	85.65	0.15	102	1339
T2	5	93.22	0.19	275	1339
T2	1	77.98	0.129	-	1339

• MANN

Tasks	shots	Acc.(%)	Test time (s)	Trainging time (s)	Memory (KB)
T1	1	88.35	0.12	90	4134

References

[1] Li, F., Chen, J., Pan, J., & Pan, T. (2020). Cross-domain learning in rotating machinery fault diagnosis under various operating conditions based on parameter transfer. Measurement Science and Technology, 31(8), 085104.  
[2] Xiao, D., Huang, Y., Zhao, L., Qin, C., Shi, H., & Liu, C. (2019). Domain adaptive motor fault diagnosis using deep transfer learning. IEEE Access, 7, 80937-80949.
[3] Finn, C., Abbeel, P., & Levine, S. (2017, July). Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning (pp. 1126-1135). PMLR.  
[4] Nichol, A., Achiam, J., & Schulman, J. (2018). On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999.  
[5] Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., & Lillicrap, T. (2016, June). Meta-learning with memory-augmented neural networks. In International conference on machine learning (pp. 1842-1850). PMLR.  
[6] Snell, J., Swersky, K., & Zemel, R. (2017, December). Prototypical networks for few-shot learning. In Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 4080-4090).  
[7] Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P. H., & Hospedales, T. M. (2018). Learning to compare: Relation network for few-shot learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1199-1208).  

3. Our related work on meta-learning in fault diagnosis

• Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis
• Similarity-based meta-learning network with adversarial domain adaptation for cross-domain fault identification
• Intelligent fault diagnosis of mechanical equipment under varying working condition via iterative matching network augmented with selective Signal reuse strategy
• Intelligent Fault Diagnosis of Satellite Communication Antenna via a Novel Meta-learning Network Combining with Attention Mechanism

If you have used our codes or pretrained models in your work, please cite our following articles.

@article{feng2021metafault,
title = {Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects},
journal = {Knowledge-Based Systems},
pages = {107646},
year = {2021},
issn = {0950-7051},
doi = {https://doi.org/10.1016/j.knosys.2021.107646},
url = {https://www.sciencedirect.com/science/article/pii/S0950705121009084},
author = {Yong Feng and Jinglong Chen and Jingsong Xie and Tianci Zhang and Haixin Lv and Tongyang Pan},
keywords = {Meta-learning, Few-shot learning, Small sample, Cross-domain, Fault diagnosis},
}