Laboratory evaluation of a low-cost micro electro-mechanical systems sensor for inclination and acceleration monitoring

Antonis Paganis, Vassiliki N. Georgiannou, Xenofon Lignos, Reina El Dahr
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Abstract

In this study, the design and development of a sensor made of low-cost parts to monitor inclination and acceleration are presented. Α micro electro-mechanical systems, micro electro mechanical systems, sensor was housed in a robust enclosure and interfaced with a Raspberry Pi microcomputer with Internet connectivity into a proposed tilt and acceleration monitoring node. Online capabilities accessible by mobile phone such as real-time graph, early warning notification, and database logging were implemented using Python programming. The sensor response was calibrated for inherent bias and errors, and then tested thoroughly in the laboratory under static and dynamic loading conditions beside high-quality transducers. Satisfactory accuracy was achieved in real time using the Complementary Filter method, and it was further improved in LabVIEW using Kalman Filters with parameter tuning. A sensor interface with LabVIEW and a 600 MHz CPU microcontroller allowed real-time implementation of high-speed embedded filters, further optimizing sensor results. Kalman and embedded filtering results show agreement for the sensor, followed closely by the low-complexity complementary filter applied in real time. The sensor's dynamic response was also verified by shaking table tests, simulating past recorded seismic excitations or artificial vibrations, indicating negligible effect of external acceleration on measured tilt; sensor measurements were benchmarked using high-quality tilt and acceleration measuring transducers. A preliminary field evaluation shows robustness of the sensor to harsh weather conditions.

Abstract Image

用于倾斜和加速度监测的低成本微机电系统传感器的实验室评估
在本研究中,设计和开发了一种由低成本零件制成的用于监测倾斜和加速度的传感器。Α微机电系统,微机电系统,传感器被安置在一个坚固的外壳中,并与具有互联网连接的树莓派微型计算机连接到拟议的倾斜和加速度监测节点。可以通过移动电话访问的在线功能,如实时图形、早期预警通知和数据库日志记录,都是使用Python编程实现的。对传感器响应进行了固有偏差和误差校准,然后在实验室中与高质量传感器一起在静态和动态加载条件下进行了全面测试。利用互补滤波方法获得了满意的实时精度,并在LabVIEW中利用参数可调的卡尔曼滤波器进一步提高了精度。传感器接口与LabVIEW和600 MHz CPU微控制器允许实时实现高速嵌入式滤波器,进一步优化传感器结果。卡尔曼滤波和嵌入式滤波结果显示传感器的一致性,其次是低复杂度的互补滤波应用于实时。传感器的动态响应也通过振动台测试得到验证,模拟过去记录的地震激励或人工振动,表明外部加速度对测量倾斜度的影响可以忽略不计;传感器测量使用高质量的倾斜和加速度测量传感器进行基准测试。初步的现场评估表明传感器对恶劣天气条件的鲁棒性。
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