Sensor Fusion Architectures for High-Accuracy Failure Investigation in MedTech
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V7I2P122Keywords:
Sensor Fusion, Failure Analysis, Medtech, Fault Detection, Anomaly Detection, ISO 13485, Digital Twin, Machine Learning, Quality Systems, Root Cause AnalysisAbstract
Medical device failures can have life-threatening consequences, yet current failure investigation protocols often rely on single-sensor or manual inspection methods that miss subtle, multi-domain fault signatures. A systematic review of sensor fusion architectures including centralized, decentralized, hierarchical, and hybrid configurations is presented as applied to high-accuracy failure investigation in medical technology (MedTech). Established data-level, feature-level, and decision-level fusion paradigms are examined, their suitability for regulatory environments governed by ISO 13485, IEC 60601, and FDA 21 CFR Part 11 is assessed, and a reference architecture integrating heterogeneous sensing modalities (vibration, acoustic emission, thermal, electrical impedance, and optical) with machine-learning-based anomaly detection is proposed. Key challenges including sensor calibration drift, real-time latency, data provenance, and explainability are discussed. A forward-looking roadmap covering edge-AI deployment, digital-twin integration, and federated learning strategies appropriate for MedTech quality systems is provided.
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