Early Detection of Faillure in Conveyor Chain Systems by Wireless Sensor Node
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Standard
IEEE SENSORS 2023: Conference Proceedings. Piscataway: Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of IEEE Sensors).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - CHAP
T1 - Early Detection of Faillure in Conveyor Chain Systems by Wireless Sensor Node
AU - Bouattour, Ghada
AU - Wang, Lidu
AU - Al-Hammouri, Sajidah
AU - Yang, Jiachen
AU - Viehweger, Christian
AU - Kanoun, Olfa
N1 - The authors would like to thank the German Federal Ministry for Economic Affairs and Climate Action and AIF for funding the project WearTrack within the Central Innovation Program for SMEs (ZIM). Also, the authors would like to thank the DAAD for funding the project “Promotion of Higher Education in Biomedical Engineering” with the grant number: 57612192. Publisher Copyright: © 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In the ever-growing industrial landscape, the early detection of failures in machines with high accuracy becomes more and more crucial and essential to safe and dependable operations. Based on this concept, a machine learning algorithm is investigated for early detection of failure for conveyor chain systems. The proposed approach is based on the integration of wireless sensor nodes in the conveyor chain to measure the vibrations. The collected data has been acquired in different working conditions including slight imbalances as well as early failure scenarios that do affect the plastic chain. The data was collected using a conveyor chain with a length of 2 meters and with programmable speed and movement scenarios. Furthermore, different loads and forces have been considered during the data collection to mimic real applications in the lab. The selection of features to avoid correlation between them is considered. After comparison between different machine learning algorithms, the C-SVM algorithm is selected with an accuracy of 96.5%, which guarantees high precision and selectivity to the failures.
AB - In the ever-growing industrial landscape, the early detection of failures in machines with high accuracy becomes more and more crucial and essential to safe and dependable operations. Based on this concept, a machine learning algorithm is investigated for early detection of failure for conveyor chain systems. The proposed approach is based on the integration of wireless sensor nodes in the conveyor chain to measure the vibrations. The collected data has been acquired in different working conditions including slight imbalances as well as early failure scenarios that do affect the plastic chain. The data was collected using a conveyor chain with a length of 2 meters and with programmable speed and movement scenarios. Furthermore, different loads and forces have been considered during the data collection to mimic real applications in the lab. The selection of features to avoid correlation between them is considered. After comparison between different machine learning algorithms, the C-SVM algorithm is selected with an accuracy of 96.5%, which guarantees high precision and selectivity to the failures.
KW - Early Failure Detection
KW - Industry 5.0
KW - Machine learning
KW - Wireless Sensor Node
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85179759992&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/c15da057-5162-37d9-9b1b-6a56cad32c63/
U2 - 10.1109/SENSORS56945.2023.10325118
DO - 10.1109/SENSORS56945.2023.10325118
M3 - Article in conference proceedings
AN - SCOPUS:85179759992
SN - 979-8-3503-0388-9
T3 - Proceedings of IEEE Sensors
BT - IEEE SENSORS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
CY - Piscataway
T2 - 2023 IEEE SENSORS, SENSORS 2023
Y2 - 29 October 2023 through 1 November 2023
ER -