A Comparative Study on Methods for Fusing Data-Driven and Physics-Based Models for Hybrid Remaining Useful Life Prediction of Air Filters

Abstract

Approaches for diagnosis and prognosis of the health of engineering systems are divided into data-driven, model-based, and hybrid methods. Data-driven methods depend on the availability of data. Model-based methods require knowledge of the degradation process. A great effort of data generation along with the high complexity of degradation processes often limits both approaches. To mitigate these limitations, the combination of data and knowledge through hybrid methods is examined in this paper. This approach is compared to the alternative approach of reducing the effort of generating training data, as both are gaining importance in diagnostics and prognostics. A new categorization of hybrid prognostic methods for combining data-driven and physics-based models is presented, along with references to existing realizations of these methods. Based on the categorization, a case study on the hybrid remaining useful life prediction of a filtration process is conducted. Several hybrid methods are implemented and tested in this study. Through the combination of models, an improvement in predictive accuracy is achieved. In addition, the paper examines systematic attributes of the individual hybrid methods. Statements on the influence of data scarcity on the predictive accuracy, data-driven models with high variance, and the computational efficiency of the hybrid methods are made. It is shown that these statements are supported by the case study’s results.

Keywords

• Data-driven methods
• filtration
• hybrid methods
• model-based methods
• physics-informed machine learning
• prognostics and health management