Fusing Physics-based and Deep Learning Models for Prognostics (2003.00732v2)

Abstract: Physics-based and data-driven models for remaining useful lifetime (RUL) prediction typically suffer from two major challenges that limit their applicability to complex real-world domains: (1) incompleteness of physics-based models and (2) limited representativeness of the training dataset for data-driven models. Combining the advantages of these two directions while overcoming some of their limitations, we propose a novel hybrid framework for fusing the information from physics-based performance models with deep learning algorithms for prognostics of complex safety-critical systems under real-world scenarios. In the proposed framework, we use physics-based performance models to infer unobservable model parameters related to a system's components health solving a calibration problem. These parameters are subsequently combined with sensor readings and used as input to a deep neural network to generate a data-driven prognostics model with physics-augmented features. The performance of the hybrid framework is evaluated on an extensive case study comprising run-to-failure degradation trajectories from a fleet of nine turbofan engines under real flight conditions. The experimental results show that the hybrid framework outperforms purely data-driven approaches by extending the prediction horizon by nearly 127\%. Furthermore, it requires less training data and is less sensitive to the limited representativeness of the dataset compared to purely data-driven approaches.

• The paper introduces a hybrid framework that integrates physics-based insights with deep learning to extend RUL prediction by 127%.
• It employs physics-derived calibration parameters combined with sensor data, enhancing model interpretability and prediction accuracy.
• Experimental validation on turbofan engine data demonstrates improved prognostic performance with reduced reliance on extensive training datasets.

Fusing Physics-Based and Deep Learning Models for Prognostics

The integration of physics-based models with deep learning algorithms for prognostics represents a notable effort towards enhancing the predictability and reliability of remaining useful lifetime (RUL) assessments in complex systems. The paper presented by Manuel Arias Chao, Chetan Kulkarni, Kai Goebel, and Olga Fink, proposes a comprehensive hybrid framework encompassing both methodologies to address the limitations facing each standalone approach.

Hybrid Framework Overview

Physics-based models are often hampered by their incomplete representation of system dynamics, while data-driven models struggle with the representativeness of real-world data. The novel framework here attempts to alleviate these limitations by harmonizing the strengths of the two methodologies. The approach uses physics-based models to infer unobservable parameters indicative of component health through calibration, which are then fed into a deep neural network (DNN) alongside sensor readings. This fusion aims to generate a prognostics model that benefits from physics-augmented features, thereby facilitating improved predictions under real-world conditions.

Experimental Validation and Results

To substantiate the framework's efficacy, the authors conducted extensive tests using a dataset of turbofan engine degradation trajectories simulated under real flight conditions. The running-to-failure data was derived using the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) model. Notably, the hybrid model demonstrated substantial improvements over traditional data-driven methods, significantly extending the RUL prediction horizon by approximately 127%, while simultaneously requiring less training data. These results demonstrate not just an enhancement in prediction quality, but also a reduction in dataset dependency—a critical advantage for real-world applications where comprehensive datasets may not be available.

Practical Implications

The presented hybrid framework brings forward several practical advantages. It offers the capacity to deliver accurate predictions even when training datasets are sparse, a situation frequently encountered in real-world applications. Moreover, it enhances model interpretability by leveraging physically meaningful input features. The robustness of this approach in handling limited data scenarios suggests that it may significantly lower the barriers to implementing RUL prediction algorithms in various engineering domains, leading to reduced maintenance costs, improved safety, and operational efficiency.

Theoretical Implications and Future Developments

From a theoretical standpoint, this research underscores the potential of integrating domain-specific knowledge encoded in physics-based strategies with the adaptive learning capabilities of neural networks. This integration could be pivotal in advancing prognostics and health management (PHM) systems, offering a pathway to more generalized models applicable across different systems and conditions. Future research could explore the transferability of such hybrid models to other domains while maintaining accuracy and efficiency. Additionally, the exploration of more sophisticated calibration techniques and the use of advanced neural network architectures could yield further improvements in prediction capabilities.

In conclusion, this framework exemplifies a significant stride in the development of prognostics models, leveraging combined methodologies to enhance prediction robustness and accuracy. As industries continue to gravitate towards predictive maintenance strategies, such hybrid models are likely to play a crucial role in advancing PHM technologies and methodologies.