A Hybrid Approach for Fusing Physics and Data for Failure Prediction

Prashanth Pillai, Anshul Kaushik, Shivanand Bhavikatti, Arjun Roy, and Virendra Kumar
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Publication Issue: 
Special Issue Big Data and Analytics
Submission Type: 
Full Paper
ijphm_16_025.pdf772.71 KBNovember 21, 2016 - 11:22pm

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.

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Submission Keywords: 
Hybrid model
data fusion
machine learning
Submission Topic Areas: 
Component-level PHM
Model-based methods for fault detection, diagnostics, and prognosis
Physics of failure
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