TY - JOUR
T1 - Online instantaneous angular speed estimation from vibration on low-power embedded systems evaluated on a gas foil bearing use case
AU - Kliemank, Martin Leonhard
AU - Rupprecht, Bernhard
AU - Ahmadzadeh, Majid
AU - Brederlow, Ralf
AU - Stahl, Karsten
AU - Liebich, Robert
AU - Vogel-Heuser, Birgit
AU - Gühmann, Clemens
N1 - Publisher Copyright:
© 2024
PY - 2025
Y1 - 2025
N2 - This study presents an algorithm for estimating the instantaneous angular speed (IAS) from vibrations in low-power embedded systems, evaluated on the use-case of a gas foil bearing. While there are numerous methods for IAS estimation, they have thus far only been evaluated using industrial-grade sensors and data acquisition systems with high computational resources. To boost the application of IAS estimation methods in embedded applications, like sensor-integrating machine elements, this paper highlights and evaluates an algorithm for low-power embedded systems. Theoretical analysis and benchmarks on various low-power microcontrollers demonstrate its feasibility for real-time application. Furthermore, evaluation with lower sampling rates and ADC resolutions confirms compatibility with small-scale sensors typical in embedded systems. For good performance, a typical Cortex-M4-based MCU (e.g. 1.12 ms estimation time using 1024 data points) and a sensor with 8-bit resolution and 6400 Hz sampling rate would suffice. These findings establish the algorithm's readiness for diverse embedded applications.
AB - This study presents an algorithm for estimating the instantaneous angular speed (IAS) from vibrations in low-power embedded systems, evaluated on the use-case of a gas foil bearing. While there are numerous methods for IAS estimation, they have thus far only been evaluated using industrial-grade sensors and data acquisition systems with high computational resources. To boost the application of IAS estimation methods in embedded applications, like sensor-integrating machine elements, this paper highlights and evaluates an algorithm for low-power embedded systems. Theoretical analysis and benchmarks on various low-power microcontrollers demonstrate its feasibility for real-time application. Furthermore, evaluation with lower sampling rates and ADC resolutions confirms compatibility with small-scale sensors typical in embedded systems. For good performance, a typical Cortex-M4-based MCU (e.g. 1.12 ms estimation time using 1024 data points) and a sensor with 8-bit resolution and 6400 Hz sampling rate would suffice. These findings establish the algorithm's readiness for diverse embedded applications.
KW - Embedded systems
KW - IAS tracking
KW - Instantaneous angular speed (IAS) estimation
KW - Low-power microcontrollers
KW - RPM estimation
UR - http://www.scopus.com/inward/record.url?scp=85213953603&partnerID=8YFLogxK
U2 - 10.1016/j.measen.2024.101600
DO - 10.1016/j.measen.2024.101600
M3 - Article
AN - SCOPUS:85213953603
SN - 2665-9174
JO - Measurement: Sensors
JF - Measurement: Sensors
M1 - 101600
ER -