# Remaining Useful Lifetime Prediction via Deep Domain Adaptation

## TL;DR

This paper introduces a deep domain adaptation method using LSTM and adversarial training to improve Remaining Useful Lifetime predictions across different operating conditions without requiring target domain failure data.

## Contribution

It proposes a novel domain adversarial neural network approach for RUL prediction that effectively handles distribution shifts between source and target domains in prognostics.

## Key findings

- Improved RUL prediction accuracy across different domains.
- Effective domain-invariant feature learning with DANN.
- Enhanced reliability of prognostics under varying conditions.

## Abstract

In Prognostics and Health Management (PHM) sufficient prior observed degradation data is usually critical for Remaining Useful Lifetime (RUL) prediction. Most previous data-driven prediction methods assume that training (source) and testing (target) condition monitoring data have similar distributions. However, due to different operating conditions, fault modes, noise and equipment updates distribution shift exists across different data domains. This shift reduces the performance of predictive models previously built to specific conditions when no observed run-to-failure data is available for retraining. To address this issue, this paper proposes a new data-driven approach for domain adaptation in prognostics using Long Short-Term Neural Networks (LSTM). We use a time window approach to extract temporal information from time-series data in a source domain with observed RUL values and a target domain containing only sensor information. We propose a Domain Adversarial Neural Network (DANN) approach to learn domain-invariant features that can be used to predict the RUL in the target domain. The experimental results show that the proposed method can provide more reliable RUL predictions under datasets with different operating conditions and fault modes. These results suggest that the proposed method offers a promising approach to performing domain adaptation in practical PHM applications.