Early Detection of Faillure in Conveyor Chain Systems by Wireless Sensor Node

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Standard

Early Detection of Faillure in Conveyor Chain Systems by Wireless Sensor Node. / Bouattour, Ghada; Wang, Lidu; Al-Hammouri, Sajidah et al.
IEEE SENSORS 2023: Conference Proceedings. Piscataway: Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of IEEE Sensors).

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Harvard

Bouattour, G, Wang, L, Al-Hammouri, S, Yang, J, Viehweger, C & Kanoun, O 2023, Early Detection of Faillure in Conveyor Chain Systems by Wireless Sensor Node. in IEEE SENSORS 2023: Conference Proceedings. Proceedings of IEEE Sensors, Institute of Electrical and Electronics Engineers Inc., Piscataway, 2023 IEEE SENSORS, SENSORS 2023, Vienna, Austria, 29.10.23. https://doi.org/10.1109/SENSORS56945.2023.10325118

APA

Bouattour, G., Wang, L., Al-Hammouri, S., Yang, J., Viehweger, C., & Kanoun, O. (2023). Early Detection of Faillure in Conveyor Chain Systems by Wireless Sensor Node. In IEEE SENSORS 2023: Conference Proceedings (Proceedings of IEEE Sensors). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SENSORS56945.2023.10325118

Vancouver

Bouattour G, Wang L, Al-Hammouri S, Yang J, Viehweger C, Kanoun O. Early Detection of Faillure in Conveyor Chain Systems by Wireless Sensor Node. In IEEE SENSORS 2023: Conference Proceedings. Piscataway: Institute of Electrical and Electronics Engineers Inc. 2023. (Proceedings of IEEE Sensors). doi: 10.1109/SENSORS56945.2023.10325118

Bibtex

@inbook{3b210d28f6ce4045a8cc54fd2f998c3b,
title = "Early Detection of Faillure in Conveyor Chain Systems by Wireless Sensor Node",
abstract = "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.",
keywords = "Early Failure Detection, Industry 5.0, Machine learning, Wireless Sensor Node, Engineering",
author = "Ghada Bouattour and Lidu Wang and Sajidah Al-Hammouri and Jiachen Yang and Christian Viehweger and Olfa Kanoun",
note = "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: {\textcopyright} 2023 IEEE.; 2023 IEEE SENSORS, SENSORS 2023 ; Conference date: 29-10-2023 Through 01-11-2023",
year = "2023",
doi = "10.1109/SENSORS56945.2023.10325118",
language = "English",
isbn = "979-8-3503-0388-9",
series = "Proceedings of IEEE Sensors",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE SENSORS 2023",
address = "United States",
url = "https://2023.ieee-sensorsconference.org",

}

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 -

Recently viewed

Publications

  1. An observer for sensorless variable valve control in camless internal combustion engines
  2. Multilevel bridge governor by using model predictive control in wavelet packets for tracking trajectories
  3. A PD regulator to minimize noise effect using a minimal variance method for soft landing control of an electromagnetic valve actuator
  4. A simple control strategy for increasing the soft bending actuator performance by using a pressure boost
  5. Use of Machine-Learning Algorithms Based on Text, Audio and Video Data in the Prediction of Anxiety and Post-Traumatic Stress in General and Clinical Populations
  6. Comparison of Trajectory Estimation Methods Based on LIDAR and Monocular Camera in a Simulated Environment
  7. Automatic feature selection for anomaly detection
  8. The Forgotten Function of Forgetting
  9. Kit based motion generator for a soft walking robot
  10. Structural Synthesis of Parallel Robots with Unguided Linear Actuators
  11. Parameterized Synthetic Image Data Set for Fisheye Lens
  12. Document assignment in multi-site search engines
  13. Detection time analysis of propulsion system fault effects in a hexacopter
  14. On the utility of indirect methods for detecting faking
  15. On the origin of passive rotation in rotational joints, and how to calculate it
  16. Homogenization methods for multi-phase elastic composites with non-elliptical reinforcements
  17. Mining Implications From Data
  18. Early Detection of Faillure in Conveyor Chain Systems by Wireless Sensor Node
  19. Trait-based approaches to analyze links between the drivers of change and ecosystem services
  20. Design, Modeling and Control of an Over-actuated Hexacopter Tilt-Rotor
  21. Robust Control of Excavation Mobile Robot with Dynamic Triangulation Vision
  22. Optimal dynamic scale and structure of a multi-pollution economy
  23. An error management perspective on audit quality
  24. A high-resolution approach for the spatiotemporal analysis of forest canopy space using terrestrial laser scanning data
  25. Obstacle Coordinates Transformation from TVS Body-Frame to AGV Navigation-Frame
  26. Impulsive Feedback Linearization for Decoupling of a Constant Disturbance with Low Relative Degree to Control Maglev Systems
  27. A Sliding Mode Control with a Bang-Bang Observer for Detection of Particle Pollution
  28. Global Finite-Time Stabilization of Planar Linear Systems With Actuator Saturation
  29. A Lyapunov based PI controller with an anti-windup scheme for a purification process of potable water
  30. The Impact of AGVs and Priority Rules in a Real Production Setup – A Simulation Study
  31. Performance of process-based models for simulation of grain N in crop rotations across Europe
  32. A Control of an Electromagnetic Actuator Using Model Predictive Control
  33. Passive Rotation Compensation in Parallel Kinematics Using Quaternions
  34. Educational reconstruction as model for the theory-based design of student-centered learning environments in electrical engineering courses
  35. An isomorphism between polynomial eigenfunctions of the transfer operator and the Eichler cohomology for modular groups
  36. A geometric approach for the design and control of an electromagnetic actuator to optimize its dynamic performance
  37. Machine vision system errors for unmanned aerial vehicle navigation
  38. Modernizing persistence–bioaccumulation–toxicity (PBT) assessment with high throughput animal-free methods
  39. Factor structure and measurement invariance of the Students’ Self-report Checklist of Social and Learning Behaviour (SSL)
  40. A Structure and Content Prompt-based Method for Knowledge Graph Question Answering over Scholarly Data
  41. Simple relay non-linear PD control for faster and high-precision motion systems with friction
  42. Controlling a Bank Model Economy by Using an Adaptive Model Predictive Control with Help of an Extended Kalman Filter
  43. Reading Comprehension as Embodied Action: Exploratory Findings on Nonlinear Eye Movement Dynamics and Comprehension of Scientific Texts
  44. WHICH ESTIMATION SITUATIONS ARE RELEVANT FOR A VALID ASSESSMENT OF MEASUREMENT ESTIMATION SKILLS
  45. Individual Scans Fusion in Virtual Knowledge Base for Navigation of Mobile Robotic Group with 3D TVS
  46. DISKNET – A Platform for the Systematic Accumulation of Knowledge in IS Research
  47. Image compression based on periodic principal components
  48. On the computation of the warping function and the torsional properties of thin-walled crosssections of prismatic beams
  49. Within-individual leaf trait variation increases with phenotypic integration in a subtropical tree diversity experiment