TY - GEN
T1 - Analysis of Acceleration Data Using Low-Power Embedded Devices to Detect Gear Faults
AU - Rupprecht, Bernhard
AU - Sendlbeck, Stefan
AU - Vogel-Heuser, Birgit
AU - Brederlow, Ralf
AU - Knoll, Erich
AU - Stahl, Karsten
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Gear condition monitoring can prevent unexpected downtimes or sudden failure of machinery. Since gear damage usually results from tooth contact, data for reliable fault detection should be acquired as close as possible to this engagement to reduce other components' disturbances (such as vibrations). One typical gear damage mechanism is pitting. Although the detection of gear pitting using acceleration data is already covered in research, methods with integrated sensors and electronics into the gear (in-situ) are still in their infancy. Most fault detection approaches still rely on external high-performance measurement systems unsuitable for in-situ integration. Thus, this paper proposes an algorithm pipeline for detecting gear pitting using acceleration data suitable for low-power embedded devices, such as Microcontrollers (MCUs). Downsampling provides the minimum required acceleration data sample rate necessary for detection. It is the basis for future work on suitable sensor and hardware selection. Finally, implementing the algorithm pipeline on a PC and a low-power ARM-Cortex M0+ MCU shows its applicability.
AB - Gear condition monitoring can prevent unexpected downtimes or sudden failure of machinery. Since gear damage usually results from tooth contact, data for reliable fault detection should be acquired as close as possible to this engagement to reduce other components' disturbances (such as vibrations). One typical gear damage mechanism is pitting. Although the detection of gear pitting using acceleration data is already covered in research, methods with integrated sensors and electronics into the gear (in-situ) are still in their infancy. Most fault detection approaches still rely on external high-performance measurement systems unsuitable for in-situ integration. Thus, this paper proposes an algorithm pipeline for detecting gear pitting using acceleration data suitable for low-power embedded devices, such as Microcontrollers (MCUs). Downsampling provides the minimum required acceleration data sample rate necessary for detection. It is the basis for future work on suitable sensor and hardware selection. Finally, implementing the algorithm pipeline on a PC and a low-power ARM-Cortex M0+ MCU shows its applicability.
UR - http://www.scopus.com/inward/record.url?scp=85174411022&partnerID=8YFLogxK
U2 - 10.1109/CASE56687.2023.10260299
DO - 10.1109/CASE56687.2023.10260299
M3 - Conference contribution
AN - SCOPUS:85174411022
T3 - IEEE International Conference on Automation Science and Engineering
BT - 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023
PB - IEEE Computer Society
T2 - 19th IEEE International Conference on Automation Science and Engineering, CASE 2023
Y2 - 26 August 2023 through 30 August 2023
ER -