Remaining Useful Lifetime Prediction via Deep Domain Adaptation

By Paulo da Costa, Alp Eren Akçay, Yingqian Zhang, Uzay Kaymak. Reliability Engineering & System Safety, 2019

346 citations recorded by the source corpus.

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.

Subfields: Control and Systems Engineering, Mechanical Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/ed2cb425300837f99af4cc188dbbfafc3391c71a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Olga Fink
2. Kai F. Goebel
3. Manuel Arias Chao
4. Chetan S. Kulkarni

In Mechanical Engineering

1. Olga Fink
2. Kai F. Goebel
3. Manuel Arias Chao

Citations in the corpus

• Fusing Physics-based and Deep Learning Models for Prognostics (2020, methodology): “It should be pointed out that under a slightly different problem formulation, a semi-supervised learning [35] or domain adaptation strategy [36] could also be potentially applied.”