评估传感器诱导噪声对基于机器学习的CNC机器切换检测的影响
Vinai George Biju1, Anna-Maria Schmitt1, Bastian Engelmann1
1Institute of Digital Engineering, Technical University of Applied Sciences Wuerzburg-Schweinfurt, 97421 Schweinfurt, Germany.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
这项研究分析了传感器数据噪声如何影响机器学习 (ML) 模型的准确性. 高斯噪声和彩色噪声被证明是有害的,而闪和棕色噪声更安全,高斯噪声显示安全的强度值.
科学领域:
- 数据科学数据科学数据科学
- 机器学习工程 机器学习工程
- 信号处理 信号处理
背景情况:
- 传感器数据噪声显著降低了机器学习 (ML) 算法的可靠性和准确性.
- 了解噪音影响对于强大的ML模型开发至关重要.
研究的目的:
- 提出一个框架来分析对ML模型准确性的各种噪音影响.
- 评估LightGBM ML模型对十种不同类型噪声的弹性.
主要方法:
- 使用了一个全面的框架,进行了广泛的实验和评估.
- 在蒙特卡洛模拟中使用统计指标采用彻底的分析方法.
- 使用OBerA项目数据,将框架应用于CNC制造机器的切换检测.
主要成果:
- 确定高斯式和彩色噪声对ML模型准确性有害.
- 将闪和棕色噪声归类为安全噪声类型.
- 发现了高斯噪声的安全噪声强度值,与其他类型不同.
结论:
- 传感器数据噪声特征极大地影响了ML模型的性能.
- 开发的框架为ML模型提供了对噪声弹性的见解.
- 这些发现适用于工业应用,如CNC机器监控.
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