对比嵌入式声发射信号分类的两种机器学习方法
Comparison of Tiny Machine Learning Techniques for Embedded Acoustic Emission Analysis
- 用原始波形或提取特征作为输入，比较模型性能
- 所有模型准确率超99%，原始信号输入更快更省电
- 适合资源受限物联网设备部署的模型选型参考
本文对比了不同输入数据格式下机器学习方法在声发射（AE）信号分类中的表现。AE信号是结构健康监测中的有效监测手段，机器学习可依据损伤机制对不同AE信号进行分类。分类可基于完整波形或从波形中提取的特定特征。然而，哪种方法更优尚不明确。为实现模型在资源受限嵌入式物联网（IoT）系统中的部署，本研究从分类准确率、内存占用、处理时间和能耗四个方面评估并比较了两种方法。通过特征提取与筛选、神经网络设计与优化，并在低功耗IoT节点上部署模型。结果表明，所有模型分类准确率均超过99%，但嵌入式特征提取计算开销大；使用原始AE信号作为输入的模型处理速度最快、能耗最低，代价是内存需求更大。
原文摘要 · Abstract (English)
This paper compares machine learning approaches with different input data formats for the classification of acoustic emission (AE) signals. AE signals are a promising monitoring technique in many structural health monitoring applications. Machine learning has been demonstrated as an effective data analysis method, classifying different AE signals according to the damage mechanism they represent. These classifications can be performed based on the entire AE waveform or specific features that have been extracted from it. However, it is currently unknown which of these approaches is preferred. With the goal of model deployment on resource-constrained embedded Internet of Things (IoT) systems, this work evaluates and compares both approaches in terms of classification accuracy, memory requirement, processing time, and energy consumption. To accomplish this, features are extracted and carefully selected, neural network models are designed and optimized for each input data scenario, and the models are deployed on a low-power IoT node. The comparative analysis reveals that all models can achieve high classification accuracies of over 99\%, but that embedded feature extraction is computationally expensive. Consequently, models utilizing the raw AE signal as input have the fastest processing speed and thus the lowest energy consumption, which comes at the cost of a larger memory requirement.
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