边端持续学习解决制造系统实时质检难题
Continual Learning at the Edge: An Agnostic IIoT Architecture
- 在工业边端部署持续学习框架，动态适应新数据
- 缓解灾难性遗忘，保持模型长期准确率
- 适合边缘计算场景的实时质量控制应用
联网设备的指数增长对传统集中式计算系统带来延迟和带宽挑战。边缘计算通过将计算靠近数据源来应对这些问题。然而，传统机器学习算法不适用于边端系统中动态连续到达的数据。增量学习为此类场景提供了良好解决方案。本文提出一种面向工业领域的边端持续学习架构，专用于制造系统的实时质量控制。通过持续学习，有效缓解灾难性遗忘，实现高效且可靠的模型更新。
原文摘要 · Abstract (English)
The exponential growth of Internet-connected devices has presented challenges to traditional centralized computing systems due to latency and bandwidth limitations. Edge computing has evolved to address these difficulties by bringing computations closer to the data source. Additionally, traditional machine learning algorithms are not suitable for edge-computing systems, where data usually arrives in a dynamic and continual way. However, incremental learning offers a good solution for these settings. We introduce a new approach that applies the incremental learning philosophy within an edge-computing scenario for the industrial sector with a specific purpose: real time quality control in a manufacturing system. Applying continual learning we reduce the impact of catastrophic forgetting and provide an efficient and effective solution.
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