Remaining Useful Lifetime Prediction via Deep Domain Adaptation (1907.07480v1)

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.

• The paper introduces a deep domain adaptation method combining LSTM and DANN for effective Remaining Useful Lifetime prediction.
• It employs adversarial training to learn domain-invariant features, significantly boosting accuracy on NASA C-MAPSS datasets.
• The approach improves PHM system reliability and reduces maintenance costs by adapting predictive models to varying operational conditions.

Overview of Remaining Useful Lifetime Prediction via Deep Domain Adaptation

The paper "Remaining Useful Lifetime Prediction via Deep Domain Adaptation" presents a novel approach to addressing the challenge of domain adaptation in prognostics and health management (PHM) systems, specifically in the context of Remaining Useful Lifetime (RUL) prediction. PHM systems are crucial for improving the reliability and availability of engineering assets while reducing maintenance costs. However, one significant limitation of existing data-driven RUL prediction methods is their assumption that both training (source) and testing (target) data are sampled from similar distributions. In real-world scenarios, this assumption often does not hold due to different operating conditions, fault modes, and noise, leading to distribution shifts across data domains.

Proposed Methodology

To circumvent the issues stemming from distribution shifts, the authors introduce a deep learning-based approach leveraging Long Short-Term Memory (LSTM) networks and Domain Adversarial Neural Networks (DANN). The proposed method is designed to learn domain-invariant features that can be transferred across domains with different operational and environmental conditions. This is achieved through an innovative use of adversarial training, where the LSTM network, as a feature extractor, is trained to produce features that confuse a domain classifier, thereby promoting domain invariance.

Experimental Setup and Results

The authors sought to validate their approach on the NASA C-MAPSS datasets, which simulate degradation data for aircraft engines under varied fault modes and operating conditions. The experimental results demonstrate that the proposed LSTM-DANN method significantly improves the reliability and accuracy of RUL predictions when applied to target datasets with varying conditions compared to models without adaptation. In particular, the method excels when transferring from source datasets with more comprehensive operational conditions to less varied target datasets, highlighting its practical applicability in real-world PHM settings.

Implications and Future Directions

The implications of this research are substantial for the development of resilient PHM systems that can handle the inherent distribution variability in real-world data. By effectively transferring knowledge from one domain to another, the LSTM-DANN framework enhances the applicability of predictive models without the need for extensive retraining on new datasets.

Theoretically, the paper contributes to the burgeoning field of domain adaptation in the context of temporal and sequence data, emphasizing the potential of adversarial learning in extracting domain-agnostic features. Practically, this approach is particularly valuable in environments where new data distributions frequently arise, such as in manufacturing or IoT applications, where equipment undergoes updates or operates under different conditions.

Looking forward, future developments could aim to refine the architecture to further improve performance and generalization capabilities across even more diverse domains. Additionally, the framework could be extended to accommodate partial or incomplete sequences, typical of online or real-time PHM applications, thus broadening its applicability. Researchers might also explore hybrid models incorporating physics-based insights to further enhance prediction accuracy by merging data-driven and domain-specific knowledge.

The work by Costa et al. demonstrates a mature handling of domain adaptation challenges and is a notable contribution to the field, blending deep learning with robust prognostic strategies to achieve advanced RUL predictions under varying operational scenarios.