# Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences

## TL;DR

This paper introduces an interpretable deep learning model using variational Bayesian inference to predict remaining useful life, combining the accuracy of machine learning with the transparency of traditional statistical methods.

## Contribution

It proposes a structured-effect neural network that offers both high interpretability and flexibility for RUL prediction, estimated through variational Bayesian inferences.

## Key findings

- Outperforms baseline methods in aircraft engine failure prediction
- Provides interpretable insights into deterioration processes
- Demonstrates superior accuracy and transparency

## Abstract

Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate preemptive maintenance decisions, whereby a failure is prevented through timely repair or replacement. This allows for a better decision support by considering the anticipated time-to-failure and thus promises to reduce costs. Here a common baseline may be derived by fitting a probability density function to past lifetimes and then utilizing the (conditional) expected remaining useful life as a prognostic. This approach finds widespread use in practice because of its high explanatory power. A more accurate alternative is promised by machine learning, where forecasts incorporate deterioration processes and environmental variables through sensor data. However, machine learning largely functions as a black-box method and its forecasts thus forfeit most of the desired interpretability. As our primary contribution, we propose a structured-effect neural network for predicting the remaining useful life which combines the favorable properties of both approaches: its key innovation is that it offers both a high accountability and the flexibility of deep learning. The parameters are estimated via variational Bayesian inferences. The different approaches are compared based on the actual time-to-failure for aircraft engines. This demonstrates the performance and superior interpretability of our method, while we finally discuss implications for decision support.