Fault Identification Using System-Level Insights and Multi-Layered Classification
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Abstract
This paper presents a novel fault detection framework for IoT device fault detection, combining meta-algorithmic decision logic with a neural network-based classifier to enable efficient, scalable failure analysis. Leveraging system-level data, the methodology adopts a multi-layered architecture grounded in systems thinking to classify devices as failed or non-failed and then identify the root cause domain—hardware, software, or firmware.
The first layer implements a meta-classifier that integrates multiple lightweight algorithms weighted by application-specific criteria such as accuracy, precision, or recall. This ensemble approach capitalizes on the strengths of diverse classifiers to enhance fault detection performance using high-level system metrics. The second layer introduces a neural network trained on subsystem-specific features—such as power metrics, SoC diagnostics, and LTE module health—to infer the most probable root cause category. This structure not only boosts classification accuracy but also captures interaction effects across subsystems through derived features.
Demonstrated on real-world telematics devices that collect GPS and vehicle diagnostic data over cellular networks, the framework addresses the need for scalable diagnostic methods in high-volume, low-failure-rate environments. By minimizing unnecessary returns and streamlining corrective action workflows, this approach delivers practical value in field operations.
The modular nature of the two-tiered architecture enables adaptability to a range of device types and fault modes, while future work will explore model generalization across varied deployments. The integration of neural networks for subsystem-level classification offers a pathway to more nuanced root cause analysis, reinforcing the importance of structured, data-driven approaches to operational reliability.
How to Cite
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Fault Identification, Classifier, meta-algorithms, failure detection
Al Mhdawi, A. K., & Al-Raweshidy, H. S. (2020). A smart optimization of fault diagnosis in electrical grid using distributed software-defined IoT system. IEEE Systems Journal, 14(2), 2780–2790. https://doi.org/10.1109/JSYST.2019.2921867
Aldaajeh, S. H., Harous, S., & Alrabaee, S. (2021). Fault-detection tactics for optimized embedded systems efficiency. IEEE Access, 9, 91328–91340. https://doi.org/10.1109/ACCESS.2021.3091617
Balakrishnan, D., Raja, J., Sudhakar, K., & Janani, K. (2023). An IoT-based system for fault detection and diagnosis in solar PV panels. In E3S web of conferences (Vol. 387, p. 05009). EDP Sciences.
Cao, X., Yang, R., Guo, C., & Wu, X. (2025). An end-to-end framework for fault detection and diagnosis electric rudder servo system-based under imbalanced data condition. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, https://doi.org/10.1177/0954410025134256
Chi, Y., Dong, Y., Wang, Z. J., Yu, F. R., & Leung, V. C. (2022). Knowledge-based fault diagnosis in industrial internet of things: a survey. IEEE Internet of Things Journal, 9(15), 12886-12900.
Dzaferagic, M., Marchetti, N., & Macaluso, I. (2022). Fault detection and classification in Industrial IoT in case of missing sensor data. IEEE Internet of Things Journal, 9(11), 8892–8900. https://doi.org/10.1109/JIOT.2021.3116785
Gan, C. L. (2020). Prognostics and Health Management of Electronics: Fundamentals, Machine Learning, and the Internet of Things: John Wiley & Sons Ltd.(2018). pp. 731, ISBN: 9781119515326 (Print), 9781119515326 (Online). Life Cycle Reliability and Safety Engineering, 9(2), 225-226.
Gertler, J. (2017). Fault detection and diagnosis in engineering systems. CRC Press. https://doi.org/10.1201/9780203756126
Ghaffarpasand, O., Burke, M., Osei, L. K., Ursell, H., Chapman, S., & Pope, F. D. (2022). Vehicle Telematics for Safer, Cleaner and More Sustainable Urban Transport: A Review. Sustainability, 14(24), 16386. https://doi.org/10.3390/su142416386
Hadi, R. H., Hady, H. N., Hasan, A. M., Al-Jodah, A., & Humaidi, A. J. (2023). Improved Fault Classification for Predictive Maintenance in Industrial IoT Based on AutoML: A Case Study of Ball-Bearing Faults. Processes, 11(5), 1507. https://doi.org/10.3390/pr11051507
Jung, Y. J., Han, S. H., & Choi, H. J. (2021). Explaining CNN and RNN using selective layer-wise relevance propagation. IEEE Access, 9, 18670-18681.
Kim, W., & Katipamula, S. (2017). A review of fault detection and diagnostics methods for building systems. Science and Technology for the Built Environment, 24(1), 3–21. https://doi.org/10.1080/23744731.2017.1318008
Lavanya, S., Prasanth, A., Jayachitra, S., & Shenbagarajan, A. (2021). A tuned classification approach for efficient heterogeneous fault diagnosis in IoT-enabled WSN applications. Measurement, 183, 109771. https://doi.org/10.1016/j.measurement.2021.109771
Leite, D., Andrade, E., Rativa, D., & Maciel, A. M. A. (2025). Fault Detection and Diagnosis in Industry 4.0: A Review on Challenges and Opportunities. Sensors, 25(1), 60. https://doi.org/10.3390/s25010060
Lo, N. G., Flaus, J. M., & Adrot, O. (2019, July). Review of machine learning approaches in fault diagnosis applied to IoT systems. In 2019 International Conference on Control, Automation and Diagnosis (ICCAD) (pp. 1-6). IEEE.
Lu, S., Lu, J., An, K., Wang, X., & He, Q. (2023). Edge computing on IoT for machine signal processing and fault diagnosis: A review. IEEE Internet of Things Journal, 10(13), 11093-11116.
Lwakatare, L. E., Raj, A., Crnkovic, I., Bosch, J., & Olsson, H. H. (2020). Large-scale machine learning systems in real-world industrial settings: A review of challenges and solutions. Information and software technology, 127, 106368. https://doi.org/10.1016/j.infsof.2020.106368
Marino, R., Wisultschew, C., Otero, A., Lanza-Gutierrez, J. M., Portilla, J., & de la Torre, E. (2021). A machine-learning-based distributed system for fault diagnosis with scalable detection quality in industrial IoT. IEEE Internet of Things Journal, 8(6), 4339–4352. https://doi.org/10.1109/JIOT.2020.3026211
Nguyen, K. T. P., Medjaher, K., & Tran, D. T. (2023). A review of artificial intelligence methods for engineering prognostics and health management with implementation guidelines. Artificial Intelligence Review, 56, 3659–3709. https://doi.org/10.1007/s10462-022-10260-y
Okes, D. (2019). Root cause analysis: The core of problem solving and corrective action. Quality Press.
Orhan, M., & Celik, M. (2024). A literature review and future research agenda on fault detection and diagnosis studies in marine machinery systems. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 238(1), 3-21.
Raouf, I., Kumar, P., Lee, H., & Kim, H. S. (2023). Transfer Learning-Based Intelligent Fault Detection Approach for the Industrial Robotic System. Mathematics, 11(4), 945. https://doi.org/10.3390/math11040945
Sangeetha, S. B., Mani, P., Maheshwari, V., Jayagopal, P., Sandeep Kumar, M., & Allayear, S. M. (2022). Design and Analysis of Multilayered Neural Network‐Based Intrusion Detection System in the Internet of Things Network. Computational Intelligence and Neuroscience, 2022(1), 9423395. https://doi.org/10.1155/2022/9423395
Seabra, J. C., Costa, M. A., & Lucena, M. M. (2016). IoT based intelligent system for fault detection and diagnosis in domestic appliances. In 2016 IEEE 6th International Conference on Consumer Electronics - Berlin (ICCE-Berlin) (pp. 205–208). IEEE. https://doi.org/10.1109/ICCE-Berlin.2016.7684756
Simske, S. J. (2013). Meta-algorithmics: patterns for robust, low cost, high quality systems. John Wiley & Sons.
Sinha, S., & Lee, Y. M. (2024). Challenges with developing and deploying AI models and applications in industrial systems. Discover Artificial Intelligence, 4, 55. https://doi.org/10.1007/s44163-024-00151-2
Smart, W. D., Grimm, C. M., & Hartzog, W. (2021). An education theory of fault for autonomous systems. Notre Dame J. on Emerging Tech., 2, 33.
Smith, D. J. (2021). Reliability, maintainability and risk: practical methods for engineers. Butterworth-Heinemann.
Stergiopoulos, G., Kotzanikolaou, P., Theocharidou, M., Lykou, G., & Gritzalis, D. (2016). Time-based critical infrastructure dependency analysis for large-scale and cross-sectoral failures. International Journal of Critical Infrastructure Protection, 12, 46–60. https://doi.org/10.1016/j.ijcip.2015.12.002
Wilson, M., & Goffnett, S. (2022). Reverse logistics: Understanding end-of-life product management. Business Horizons, 65(5), 643-655.
Xu, G., Liu, M., Jiang, Z., Shen, W., & Huang, C. (2019). Online fault diagnosis method based on transfer convolutional neural networks. IEEE Transactions on Instrumentation and Measurement, 69(2), 509-520.
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