一个新的双质量MEMS陀螺仪故障诊断平台
Rang Cui1, Tiancheng Ma2, Wenjie Zhang2
1Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, North University of China, Taiyuan 030051, China.
Micromachines
|June 28, 2023
概括
这项研究引入了一种新的自我反框架,用于诊断微电机系统 (MEMS) 陀螺仪的故障. 开发的平台在识别七种故障类型方面取得了很高的准确性,证明了它对可靠的惯性导航系统的有效性.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 机器人技术 机器人技术 机器人技术
背景情况:
- 微电机系统 (MEMS) 陀螺仪对于惯性导航系统至关重要.
- 确保MEMS陀螺仪的高可靠性对于稳定的系统运行至关重要.
- 在陀螺仪故障数据集的获取和生产成本方面存在挑战.
研究的目的:
- 为MEMS陀螺仪故障诊断提出一个自我反开发框架.
- 使用模拟和真实数据设计和验证故障诊断平台.
- 识别和分类七种不同类型的陀螺仪信号和故障.
主要方法:
- 开发了一个故障诊断平台,集成了双质量MEMS陀螺仪,Simulink模型和测量系统.
- 采用数据特征提取技术进行信号分析.
- 使用并比较了六种分类算法 (ELM,SVM,KNN,NB,NN,DTA) 进行故障预测.
主要成果:
- 该平台成功识别和分类了七种信号类型:正常,偏差,阻断,漂移,多重性,循环和内部故障.
- 极端学习机器 (ELM) 和支持矢量机器 (SVM) 算法表现出最佳性能.
- ELM和SVM算法实现了高达92.86%的测试集精度.
结论:
- 拟议的自我反框架和开发的平台对于MEMS陀螺仪故障诊断是有效的.
- 对于准确的故障分类,ELM和SVM算法显示出显著的前景.
- 该ELM算法成功地识别了现实数据集中的实际漂移故障,证实了该平台的实际实用性.
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