Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics

TL;DR

This paper presents a modified Gaussian Process Regression model that enhances RUL interval prediction accuracy in aeroengine prognostics by integrating temporal feature extraction and interpretability for better uncertainty modeling.

Contribution

It introduces a novel GPR-based approach combined with deep learning for improved RUL prediction and interpretability in manufacturing systems.

Findings

Effective capture of time-series patterns

Improved confidence interval estimation

Enhanced feature significance evaluation

Abstract

The estimation of Remaining Useful Life (RUL) plays a pivotal role in intelligent manufacturing systems and Industry 4.0 technologies. While recent advancements have improved RUL prediction, many models still face interpretability and compelling uncertainty modeling challenges. This paper introduces a modified Gaussian Process Regression (GPR) model for RUL interval prediction, tailored for the complexities of manufacturing process development. The modified GPR predicts confidence intervals by learning from historical data and addresses uncertainty modeling in a more structured way. The approach effectively captures intricate time-series patterns and dynamic behaviors inherent in modern manufacturing systems by coupling GPR with deep adaptive learning-enhanced AI process models. Moreover, the model evaluates feature significance to ensure more transparent decision-making, which is crucial for optimizing manufacturing processes. This comprehensive approach supports more accurate RUL predictions and provides transparent, interpretable insights into uncertainty, contributing to robust process development and management.