研究基于神经网络的模型,用于MEMS加速度计的非线性温度漂移补偿
1School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China.
The Review of scientific instruments
|November 20, 2024
概括
本研究引入了一种用于深孔钻探的MEMS惯性传感器的新型温度补偿模型. 帐-SSA-BPNN模型显著减少了温度漂移误差,改善了在高温环境中的钻井管道态度解决方案.
科学领域:
- 地质物理学 地质物理学
- 传感器技术 传感器技术
- 人工智能的人工智能
背景情况:
- 下洞仪表要求MEMS惯性传感器的准确性越来越高.
- 在测量过程中传感器的温度偏移在钻探过程中累积影响钻井管道态度解决方案.
- 加速度计输出显示了局部线性和全球非线性特征.
研究的目的:
- 开发一个有效的温度补偿模型,用于MEMS惯性传感器的下孔应用.
- 解决温度偏移对钻井管道态度解决方案的累积影响.
- 为了提高深孔测量的准确性和可靠性,在高温环境中.
主要方法:
- 提出了一种使用反向传播 (BP) 神经网络的温度补偿模型,该神经网络是通过一种包含帐混乱映射 (Tent-SSA-BPNN) 的子搜索算法 (SSA) 进行优化.
- 优化了SSA群体的统一性和搜索功能,使用帐混乱映射.
- 训练了Tent-SSA-BPNN模型以优化BP神经网络的权重和偏差以补偿温度.
主要成果:
- 帐-SSA-BPNN模型表现出良好的整体补偿性能,将平均绝对百分比误差从2%降至0.2%.
- 与之前改进的BP模型相比,改进的补偿模型显示了较低的平均绝对误差和根平均平方误差.
- 集成到微程序控制单元中的实时补偿测试验证了该模型的有效性.
结论:
- 拟议的帐-SSA-BPNN模型提供了一种有价值的方法,用于在高温下井环境中低成本,高精度的温度补偿.
- 这种方法大大减少了时间和测量成本.
- 该研究为开发在具有挑战性的条件下准确的传感器补偿策略提供了一个参考.
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