A Time-to-Failure Aligned Methodological Framework for Sensor Degradation Analysis and Remaining Useful Life Prediction in Industrial IoT Systems
Hathairat Ketmaneechairat
Progress in Computing Applications, Vol. 15, No. 1, 2026.
DOI: https://doi.org/10.6025/pca/2026/15/1/1-17
Link: https://www.dline.info/pca/fulltext/v15n1/pcav15n1_1.pdf
Abstract:
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.