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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.17454 |
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| _version_ | 1866929729871806464 |
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| author | Soto, Marcos |
| author_facet | Soto, Marcos |
| contents | In industrial environments, data acquisition accuracy is crucial for process control and optimization. Wireless telemetry has proven to be a valuable tool for improving efficiency in well-testing operations, enabling bidirectional communication and real-time control of downhole tools. However, high sampling frequencies present challenges in telemetry, including data storage, transmission, computational resource consumption, and battery life of wireless devices. This study explores how optimizing data acquisition strategies can reduce aliasing effects and systematic errors while improving sampling rates without compromising measurement accuracy. A reduction of 80% in sampling frequency was achieved without degrading measurement quality, demonstrating the potential for resource optimization in industrial environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17454 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Smart Sampling Strategies for Wireless Industrial Data Acquisition Soto, Marcos Signal Processing Artificial Intelligence Systems and Control In industrial environments, data acquisition accuracy is crucial for process control and optimization. Wireless telemetry has proven to be a valuable tool for improving efficiency in well-testing operations, enabling bidirectional communication and real-time control of downhole tools. However, high sampling frequencies present challenges in telemetry, including data storage, transmission, computational resource consumption, and battery life of wireless devices. This study explores how optimizing data acquisition strategies can reduce aliasing effects and systematic errors while improving sampling rates without compromising measurement accuracy. A reduction of 80% in sampling frequency was achieved without degrading measurement quality, demonstrating the potential for resource optimization in industrial environments. |
| title | Smart Sampling Strategies for Wireless Industrial Data Acquisition |
| topic | Signal Processing Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2502.17454 |