Towards Responsible AI: Evaluating Intelligent Models for Sensor Fault Detection Through the Lens of Sustainability and Performance Optimization - 2025
[en] This article introduces a methodological
framework for evaluating intelligent models for fault detection
by considering both technical performance and sustainability
aspects. The impact of model design selection, such as model
complexity and hyperparameter optimization, was examined in
terms of accuracy, robustness, and sustainability. Through a
manipulator robot, an analytical comparison is conducted of
several configurations of Machine Learning (ML) models,
including K-Nearest Neighbours (KNN), Random Forest
Regression (RFR), and Support Vector Regression (SVR), and
their performance to design parameters. The results highlight
that the KNN model effectively balances technical performance
and sustainability. This work contributes to the development of
AI systems that align with the principles of sustainable design
and reliable performance in smart manufacturing.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
ALDRINI, Joma ✱; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
CHIHI, Inès ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
✱ These authors have contributed equally to this work.
External co-authors :
no
Language :
English
Title :
Towards Responsible AI: Evaluating Intelligent Models for Sensor Fault Detection Through the Lens of Sustainability and Performance Optimization
Publication date :
22 August 2025
Event name :
2025 International Conference on Sustainability, Innovation & Technology (ICSIT)
J. Aldrini, I. Chihi, and L. Sidhom, "Fault diagnosis and self-healing for smart manufacturing: A review, " J. Intell. Manuf., Jul. 2023, doi: 10.1007/s10845-023-02165-6.
R. Hamdani and I. Chihi, "Adaptive human-computer interaction for industry 5.0: A novel concept, with comprehensive review and empirical validation, " Comput. Ind., vol. 168, p. 104268, Jun. 2025, doi: 10.1016/j.compind.2025.104268.
V. Bolon-Canedo, L. Moran-Fernandez, B. Cancela, and A. Alonso-Betanzos, "A review of green artificial intelligence: Towards a more sustainable future, " Neurocomputing, vol. 599, p. 128096, Sep. 2024, doi: 10.1016/j.neucom.2024.128096.
P. Henderson, J. Hu, J. Romoff, E. Brunskill, D. Jurafsky, and J. Pineau, "Towards the systematic reporting of the energy and carbon footprints of machine learning, " J Mach Learn Res, vol. 21, no. 1, p. 248:10039-248:10081, Jan. 2020.
L. Hennig, T. Tornede, and M. Lindauer, "Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks, " Apr. 04, 2024, arXiv: ArXiv:2404.01965. doi: 10.48550/arXiv.2404.01965.
C.-J. Wu et al., "Sustainable AI: Environmental Implications, Challenges and Opportunities, " Jan. 09, 2022, arXiv: ArXiv:2111.00364. doi: 10.48550/arXiv.2111.00364.
A. H. Sabry and U. A. B. Ungku Amirulddin, "A review on fault detection and diagnosis of industrial robots and multi-axis machines, " Results Eng., vol. 23, p. 102397, Sep. 2024, doi: 10.1016/j.rineng.2024.102397.
J. Aldrini, A. Anil Kumar, I. Chihi, and L. Sidhom, "Intermittent fault detection for MIMO systems: A case study on SCARA robot, " in International Conference on Computer Vision and Internet of Things 2023 (ICCVIoT'23), Dec. 2023, pp. 133-140. doi: 10.1049/icp.2023.2866.
M. Shahin, F. F. Chen, A. Hosseinzadeh, and N. Zand, "Using machine learning and deep learning algorithms for downtime minimization in manufacturing systems: An early failure detection diagnostic service, " Int. J. Adv. Manuf. Technol., vol. 128, no. 9, Art. no. 9, Aug. 2023, doi: 10.1007/s00170-023-12020-w.
Q. Yang, X. Li, Y. Wang, A. Ainapure, and J. Lee, "Fault Diagnosis of Ball Screw in Industrial Robots Using Non-Stationary Motor Current Signals, " Procedia Manuf., vol. 48, pp. 1102-1108, Jan. 2020, doi: 10.1016/j.promfg.2020.05.151.
R. Natarajan, S. R. P, S. C. Bose, H. L. Gururaj, F. Flammini, and S. Velmurugan, "Fault detection and state estimation in robotic automatic control using machine learning, " Array, vol. 19, p. 100298, Sep. 2023, doi: 10.1016/j.array.2023.100298.
R. Kermenov, G. Nabissi, S. Longhi, and A. Bonci, "Anomaly Detection and Concept Drift Adaptation for Dynamic Systems: A General Method with Practical Implementation Using an Industrial Collaborative Robot, " Sensors, vol. 23, no. 6, Art. no. 6, Mar. 2023, doi: 10.3390/s23063260.
Y. Li and J. Fei, "Construction of Mining Robot Equipment Fault Prediction Model Based on Deep Learning, " Electronics, vol. 13, no. 3, Art. no. 3, Jan. 2024, doi: 10.3390/electronics13030480.
T. Ye et al., "Toward Multimodal Graph Sequence Generation: A Denoising Diffusion Approach for Wheeled Robot Fault Diagnosis, " IEEE Internet Things J., vol. 12, no. 12, pp. 19605-19614, Jun. 2025, doi: 10.1109/JIOT.2025.3541337.
Z. Zhou, Z. Li, Z. Cai, and P. Wang, "Fault Identification Using Fast k-Nearest Neighbor Reconstruction, " Processes, vol. 7, no. 6, Art. no. 6, Jun. 2019, doi: 10.3390/pr7060340.
E. Wescoat, S. Kerner, and L. Mears, "A comparative study of different algorithms using contrived failure data to detect robot anomalies, " Procedia Comput. Sci., vol. 200, pp. 669-678, Jan. 2022, doi: 10.1016/j.procs.2022.01.265.
M. Ogunsanya, J. Isichei, and S. Desai, "Grid search hyperparameter tuning in additive manufacturing processes, " Manuf. Lett., vol. 35, pp. 1031-1042, Aug. 2023, doi: 10.1016/j.mfglet.2023.08.056.
Y. Song, W. Zheng, Y. Lu, and J. Song, "A Data-Driven Fault Detectability Analysis Approach Based on Actuator and Sensor Performance, " in 2024 9th International Conference on Automation, Control and Robotics Engineering (CACRE), Jul. 2024, pp. 1-5. doi: 10.1109/CACRE62362.2024.10635020.
A. Kumar and A. K. Ghosh, "Ensemble Machine Learning Methods for Unsteady Aerodynamics Modeling from Flight Data, " in 2024 10th International Conference on Control, Automation and Robotics (ICCAR), Apr. 2024, pp. 219-225. doi: 10.1109/ICCAR61844.2024.10569721.
N. Cartocci, F. Crocetti, G. Costante, P. Valigi, M. R. Napolitano, and M. L. Fravolini, "Data-Driven Sensor Fault Diagnosis Based on Nonlinear Additive Models and Local Fault Sensitivity, " in 2021 20th International Conference on Advanced Robotics (ICAR), Dec. 2021, pp. 750-756. doi: 10.1109/ICAR53236.2021.9659449.
