Edge-Cloud Alarm Level of Heterogeneous IIoT Devices Based on Knowledge Distillation in Smart Manufacturing
Abstract
:1. Introduction
- To solve the bottleneck problem, we propose a cloud–fog–edge-distributed network that connects with heterogeneous devices in industrial sites that is based on a fog–edge, rather than a cloud-based, central control;
- We propose a knowledge distillation-based algorithm to efficiently apply deep-learning-based algorithms that require a lot of computing resources for the IIoT system;
- For detailed verification, we propose a soft-label-based alarm level to provide a smooth network connection and an accurate verification method for the algorithm.
2. Background and Related Work
2.1. Cloud–Fog–Edge Computing
2.2. Knowledge Distillation
2.3. Industrial Alarm Level
3. Cloud–Fog–Edge Alarm System Using Knowledge Distillation
3.1. Cloud–Fog–Edge Alarm-Level-Based Heterogeneous Device Knowledge Distillation
3.2. Soft-Label-Based Alarm Level
4. Experimental Environment
4.1. Dataset
4.2. Evaluation Metrics
5. Experiment and Result
5.1. CWRU Dataset
5.2. Casting Dataset
5.3. Experiment and Results
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Quantile | Range |
---|---|
Q1 | 0.705 |
Q2 | 0.853 |
Q3 | 0.901 |
Q4 | 0.954 |
Hardware Environment | Software Environment | |
---|---|---|
Cloud Computing | CPU: Intel Core i7-8700k, 3.7 Ghz, six-core twelve threads 16 GB GPU: Geforce RTX 2070 | Windows Tensorflow 2.0 Python 3.7 |
Fog Computing | CPU: Intel Core i7-8700k, 3.7 Ghz, six-core twelve threads 16 GB | Windows Tensorflow 2.0 Python 3.7 |
Edge Computing | CPU: four-core ARM A57 1.43 GHz 128 Core Maxwell 482 GFLOPs (FP16) | Linux Tensorflow 2.0 Python 3.6 |
Model | ACC Top1 | F1-Score | MCC | |
---|---|---|---|---|
Teacher | LSTM | 97.81% | 96.32% | 95.87% |
GRU | 95.52% | 93.72% | 93.22% | |
Student | LSTM | 96.77% | 95.21% | 94.76% |
GRU | 92.97% | 91.37% | 90.87% |
Model | Level 1 (%) | Level 2 (%) | Level 3 (%) |
---|---|---|---|
Student (Fog) LSTM | 90 | 4 | 6 |
Teacher (Cloud) LSTM | 96 | 2 | 2 |
Student (Fog) GRU | 84 | 9 | 7 |
Teacher (Cloud) GRU | 92 | 5 | 3 |
Model | ACC Top1 | F1-Score | MCC | |
---|---|---|---|---|
Teacher | CNN | 94.79% | 93.39% | 92.91% |
AE | 95.58% | 94.12% | 93.72% | |
Student | CNN | 90.02% | 88.52% | 88.02% |
AE | 93.92% | 92.47% | 92.05% |
Model | Level 1 (%) | Level 2 (%) | Level 3 (%) |
---|---|---|---|
Student (Fog) AE | 88 | 2 | 10 |
Teacher (Cloud) AE | 93 | 4 | 3 |
Student (Fog) CNN | 68 | 5 | 27 |
Teacher (Cloud) CNN | 91 | 3 | 6 |
Model | ACC Top1 | F1-Score | MCC | |
---|---|---|---|---|
Teacher | LSTM | 97.81% | 96.32% | 95.87% |
GRU | 95.52% | 93.72% | 93.22% | |
AE | 95.58% | 94.12% | 93.72% | |
CNN | 94.79% | 93.39% | 92.91% | |
Student | LSTM | 96.77% | 95.21% | 94.76% |
GRU | 92.97% | 91.37% | 90.87% | |
AE | 93.92% | 92.47% | 92.05% | |
CNN | 90.02% | 88.52% | 88.02% |
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Oh, S.; Kim, D.; Lee, C.; Jeong, J. Edge-Cloud Alarm Level of Heterogeneous IIoT Devices Based on Knowledge Distillation in Smart Manufacturing. Electronics 2022, 11, 899. https://doi.org/10.3390/electronics11060899
Oh S, Kim D, Lee C, Jeong J. Edge-Cloud Alarm Level of Heterogeneous IIoT Devices Based on Knowledge Distillation in Smart Manufacturing. Electronics. 2022; 11(6):899. https://doi.org/10.3390/electronics11060899
Chicago/Turabian StyleOh, Seokju, Donghyun Kim, Chaegyu Lee, and Jongpil Jeong. 2022. "Edge-Cloud Alarm Level of Heterogeneous IIoT Devices Based on Knowledge Distillation in Smart Manufacturing" Electronics 11, no. 6: 899. https://doi.org/10.3390/electronics11060899
APA StyleOh, S., Kim, D., Lee, C., & Jeong, J. (2022). Edge-Cloud Alarm Level of Heterogeneous IIoT Devices Based on Knowledge Distillation in Smart Manufacturing. Electronics, 11(6), 899. https://doi.org/10.3390/electronics11060899