Fault Tree and Markov Chain-Based for Instance-Specific Reliability of Angle Grinders for Circularity

SUMMARY & CONCLUSIONSThe transition to a Circular Factory (CF) introduces new challenges in reliability analysis, particularly for returned products, where degradation history and failure behavior vary significantly between individual units. Unlike new products, returned angle grinders exhibit heterogeneous failure risks due to different operational histories, wear levels, and maintenance conditions, making their reliability assessment more complex. Traditional reliability models that assume homogeneous degradation rates across products fall short in capturing the unique failure risks posed by returned items. Therefore, instance-specific reliability models are essential to accurately estimate failure probabilities, and reliability metrics, enabling data-driven decision-making for product reprocessing under uncertainty across product generations. This study addresses this challenge by integrating Fault Tree Analysis (FTA) and Markov Chains to model system- and subsystem-level reliability of returned angle grinders within the CF framework.In this study, we first applied the Contact and Channel (C&C2) approach to map embodiment–function relations and visualize interactions between system components and their interfaces. We then employed a Reliability Block Diagram (RBD) to model the system’s connectivity in series–parallel configurations, allowing identification of critical dependencies in overall reliability. Excessive vibration was selected as the failure mode due to its significant impact on performance and safety in returned products. A comprehensive FTA was conducted to systematically identify possible root causes. To move beyond the static and deterministic nature of FTA, we translated its probability structure into a stochastic Markov Chain model. This allowed time-dependent degradation representation, where the system transitions between defined states of health, degradation, and failure with estimated transition probabilities. By incorporating progressive deterioration patterns, the Markov model offers a more realistic reliability prediction over successive operational and reprocessing cycles. The integrated C&C2–RBD–FTA–Markov Framework provides a robust framework for analyzing instance-specific reliability at both system and subsystem levels in the CF context. It supports informed decisions on whether to remanufacture, refurbish, or scrap returned units, while offering a transferable approach for other product categories.