电化学传感器的数据驱动虚拟传感器
Lucia Sangiorgi1, Veronica Sberveglieri2,3, Claudio Carnevale1
1Department of Mechanical and Industrial Engineering, University of Brescia, 25123 Brescia, Italy.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
机器学习模型可以预测和管理用于乙醇测量的电化学传感器的故障. 这种方法重建传感器数据并识别冗余,改善监控和管理.
科学领域:
- 电化学 电化学 电化学
- 机器学习 机器学习
- 传感器技术 传感器技术
背景情况:
- 电化学传感器产生大量数据,使先进的机器学习/人工智能 (AI) 应用成为可能.
- 机器学习模型可以重现传感器数据,并预测/管理传感器故障.
- 使用机器学习的虚拟传感正在改变信息监控和管理.
研究的目的:
- 应用数据驱动模型来评估电化学乙醇传感器中的传感器冗余性.
- 使用机器学习预测和管理电化学传感器的故障.
- 为了重建传感器数据和评估故障后的性能.
主要方法:
- 利用自回归模型和人工神经网络进行数据驱动分析.
- 开发模型来评估一系列六个电化学传感器中的传感器冗余性.
- 实施了用于传感器数据重建的故障预测和管理策略.
主要成果:
- 从两个电化学传感器中成功重建了测量值.
- 展示了在模拟故障后重现传感器值的能力.
- 在故障管理的性能和灵敏度分析中取得了令人鼓舞的结果.
结论:
- 数据驱动的机器学习模型有效评估冗余性,并管理电化学乙醇传感器的故障.
- 拟议的方法提高了传感器系统的可靠性和稳定性.
- 这种方法为先进的传感器数据监控和故障容忍提供了有前途的解决方案.
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