WTNN: Weibull-Tailored Neural Networks for survival analysis

TL;DR

This paper introduces WTNN, a neural network framework tailored for Weibull survival analysis, capable of handling censored data and incorporating prior knowledge to produce robust, interpretable survival predictions.

Contribution

The paper presents WTNN, a novel neural network architecture specifically designed for Weibull survival modeling that integrates qualitative prior knowledge and handles censored data.

Findings

WTNN can be reliably trained on proxy and censored data.

The approach produces robust and interpretable survival predictions.

WTNN outperforms existing methods in numerical experiments.

Abstract

The Weibull distribution is a commonly adopted choice for modeling the survival of systems subject to maintenance over time. When only proxy indicators and censored observations are available, it becomes necessary to express the distribution's parameters as functions of time-dependent covariates. Deep neural networks provide the flexibility needed to learn complex relationships between these covariates and operational lifetime, thereby extending the capabilities of traditional regression-based models. Motivated by the analysis of a fleet of military vehicles operating in highly variable and demanding environments, as well as by the limitations observed in existing methodologies, this paper introduces WTNN, a new neural network-based modeling framework specifically designed for Weibull survival studies. The proposed architecture is specifically designed to incorporate qualitative prior knowledge regarding the most influential covariates, in a manner consistent with the shape and structure of the Weibull distribution. Through numerical experiments, we show that this approach can be reliably trained on proxy and right-censored data, and is capable of producing robust and interpretable survival predictions that can improve existing approaches.