Rashomon perspective for measuring uncertainty in the survival predictive maintenance models

TL;DR

This paper introduces a Rashomon-based approach to quantify uncertainty in survival models for aircraft engine RUL prediction, emphasizing model multiplicity to improve reliability in predictive maintenance.

Contribution

It presents a novel Rashomon survival curve method to capture model agreement and uncertainty, advancing uncertainty quantification in survival analysis for maintenance.

Findings

Longer censoring increases prediction variability.

Model multiplicity reduces risk in RUL estimation.

Rashomon approach enhances robustness of failure predictions.

Abstract

The prediction of the Remaining Useful Life of aircraft engines is a critical area in high-reliability sectors such as aerospace and defense. Early failure predictions help ensure operational continuity, reduce maintenance costs, and prevent unexpected failures. Traditional regression models struggle with censored data, which can lead to biased predictions. Survival models, on the other hand, effectively handle censored data, improving predictive accuracy in maintenance processes. This paper introduces a novel approach based on the Rashomon perspective, which considers multiple models that achieve similar performance rather than relying on a single best model. This enables uncertainty quantification in survival probability predictions and enhances decision-making in predictive maintenance. The Rashomon survival curve was introduced to represent the range of survival probability estimates, providing insights into model agreement and uncertainty over time. The results on the CMAPSS dataset demonstrate that relying solely on a single model for RUL estimation may increase risk in some scenarios. The censoring levels significantly impact prediction uncertainty, with longer censoring times leading to greater variability in survival probabilities. These findings underscore the importance of incorporating model multiplicity in predictive maintenance frameworks to achieve more reliable and robust failure predictions. This paper contributes to uncertainty quantification in RUL prediction and highlights the Rashomon perspective as a powerful tool for predictive modeling.