在微控制器上实现高精度铣削质量监测，仅需15毫秒推理时间。
TinyML Towards Industry 4.0: Resource-Efficient Process Monitoring of a Milling Machine
- 构建微型机器学习全流程，从数据生成到嵌入式部署
- 8位量化模型仅占12.59kiB内存，推理耗时15.4ms，功耗1.462mJ
- 适用于工业4.0场景下的低成本、低功耗设备监控
在工业4.0背景下，可通过无线监测系统为长期服役的工业设备添加过程监控能力。本文提出一套完整的TinyML流程，涵盖数据集构建、模型开发、预处理与分类管道的嵌入式部署与评估。首先简要回顾了TinyML在工业过程监控中的应用；随后介绍了新型的MillingVibes数据集。通过设计一个8位量化卷积神经网络（CNN），实现了结构集成的过程质量监控可行性：该模型参数存储量仅为12.59kiB，测试准确率达100.0%，在ARM Cortex M4F微控制器上完成一次推理仅需15.4ms，能耗为1.462mJ，为未来基于TinyML的工业过程监控方案提供了参考基准。
原文摘要 · Abstract (English)
In the context of industry 4.0, long-serving industrial machines can be retrofitted with process monitoring capabilities for future use in a smart factory. One possible approach is the deployment of wireless monitoring systems, which can benefit substantially from the TinyML paradigm. This work presents a complete TinyML flow from dataset generation, to machine learning model development, up to implementation and evaluation of a full preprocessing and classification pipeline on a microcontroller. After a short review on TinyML in industrial process monitoring, the creation of the novel MillingVibes dataset is described. The feasibility of a TinyML system for structure-integrated process quality monitoring could be shown by the development of an 8-bit-quantized convolutional neural network (CNN) model with 12.59kiB parameter storage. A test accuracy of 100.0% could be reached at 15.4ms inference time and 1.462mJ per quantized CNN inference on an ARM Cortex M4F microcontroller, serving as a reference for future TinyML process monitoring solutions.
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