A Hybrid Approach for Fusing Physics and Data for Failure Prediction

Abstract

This work describes the architecture for developing physics of failure models, derived as a function of machine sensor data, and integrating with data pertaining to other relevant factors like geography, manufacturing, environment, customer and inspection information, that are not easily modeled using physics principles. The mechanics of the system is characterized using surrogate models for stress and metal temperature based on results from multiple non-linear finite element simulations. A cumulative damage index measure has been formulated that quantifies the health of the component. To address deficiencies in the simulation results, a model tuning framework is designed to improve the accuracy of the model. Despite the model tuning, un-modelled sources of variation can lead to insufficient model accuracy. It is required to incorporate these un-modelled effects so as to improve the model performance. A novel machine learning based model fusion approach has been presented that can combine physics model predictions with other data sources that are difficult to incorporate in a physics framework. This approach has been applied to a gas turbine hot section turbine blade failure prediction example.

Prognostics and Health Management for Maintenance Practitioners - Review, Implementation and Tools Evaluation

“…They claimed that proposed hybrid approach could predict RUL of MEMS effectively. The authors in (Pillai, Kaushik, Bhavikatti, Roy, & Kumar, 2016) proposed a hybrid approach for gas turbine failure prognostics based on machine learning.…”

Unscented Kalman Filtering for Prognostics Under Varying Operational and Environmental Conditions

“…In contrast, data-driven models are easier to implement: no physical model is required, but data unravels hidden patterns (Elattar et al, 2016). However, this requires large quantities of failure data that are not always available because failures are generally prevented (Pillai et al, 2016). Furthermore, the data sets need to include all possible degradations for all conditions (i.e.…”

Technologies to Reactors: Enabling Accelerated Deployment of Nuclear Energy Systems Workshop Report

“…More likely, a hybrid approach combining physics-based, data-driven analysis will be employed. 40 Physics-based and empirical PHM models must provide accurate and robust uncertainty quantification in order to be useful for short-term autonomous control decisions and long-term autonomous operation planning. Work remains to be done to define and develop appropriate approaches to uncertainty quantification for autonomous decision making.…”