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.
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2024 The Author(s). Deep Underground Science and Engineering published by John Wiley & Sons Australia, Ltd on behalf of China University of Mining and Technology.
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