Progress in Computing Applications Hathairat Ketmaneechairat 15 1 2026 https://doi.org/10.6025/pca/2026/15/1/1-17 https://www.dline.info/pca/fulltext/v15n1/pcav15n1_1.pdf This study presents a Time to Failure (TTF) aligned methodological framework for analyzing sensor degradation and predicting Remaining Useful Life (RUL) in Industrial Internet of Things (IIoT) systems. Addressing critical challenges of sensor drift and measurement uncertainty, the proposed architecture employs a seven layer pipeline to transform noisy, high frequency telemetry into actionable health indicators. A key innovation is the shift from absolute cycle alignment to TTF aligned trajectory construction, which mitigates survivorship bias and reveals consistent degradation signatures that are otherwise obscured during early operational life. Empirical validation on smart manufacturing datasets (FD001 subset) demonstrates the framework's efficacy: statistical metrics including effect size and Pearson correlation identified sensor_11 and sensor_4 as primary degradation indicators, exhibiting monotonic trends and low inter engine variability. Predictive modeling via Random Forest regression confirmed that TTF aligned features significantly enhance failure prediction accuracy compared to raw sensor inputs. Furthermore, phase aware degradation analysis using change point detection enables robust health state segmentation by distinguishing healthy and transition phases. Static sensors were systematically excluded to reduce computational overhead while preserving prognostic relevance. The framework bridges raw sensor data and reliable decision support, enabling proactive maintenance scheduling that minimizes unplanned downtime and optimizes operational safety in Industry 4.0 environments. Scalability is ensured through containerized inference services, while encryption protocols protect sensitive industrial data. Future work should explore generative models for data recovery and expand validation across heterogeneous fleets to enhance resilience in complex industrial networks, ultimately supporting sustained production quality through data driven prognostics.