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 -