OASIS-UROS: Open acquisition system for IEPE sensors - upgraded, refined, and overhauled software
Oliver Maximilian Zobel, Johannes Maierhofer, Andreas Köstler, Daniel J. Rixen
TL;DR
OASIS-UROS is an upgraded open-source data acquisition system for IEPE sensors, offering improved performance and flexibility for budget-conscious users.
Contribution
The paper introduces an enhanced open-source IEPE sensor acquisition system with improved hardware and software capabilities.
Findings
OASIS-UROS achieves a sample rate of up to 36 kHz across all eight channels.
The system performs comparably to commercial systems in some experimental modal analysis aspects.
The system is a viable alternative for budget-constrained users needing adaptability and transparency.
Abstract
OASIS-UROS continues the previously published Open Acquisition System for IEPE Sensors (OASIS). While still building on the ESP32 microcontroller, this version improves the overall performance by switching to an SD card caching system and upgrading the analog-digital converter to an AD7606C-18, which has a higher resolution, provides eight channels, oversampling, and software-adjustable voltage ranges. Also improved is the IEPE front-end and power supply, as well as the firmware of the acquisition system, which can now achieve a sample rate of up to 36 kHz while sampling all eight channels. This paper documents the hardware and software of OASIS-UROS and provides all materials required to reproduce the open acquisition system. Lastly, the system was validated against commercial hardware and software in an experimental modal analysis context. This showed that the system performs close to…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27
Figure 28
Figure 29
Figure 30
Figure 31
Figure 32
Figure 33
Figure 34Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSensor Technology and Measurement Systems · Experimental Learning in Engineering · Advanced MEMS and NEMS Technologies