The objective of this research is to advance the state of predictive science by integrating physics-based stochastic modeling methods with data analytic techniques to improve the accuracy and reliability of probabilistic prediction of event occurrence in dynamical systems, such as manufacturing machines or processes.
To accomplish the above described objective, a hybrid modeling method is developed in this research, which integrates physical models for system performance degradation with stochastic modeling (realized by Bayesian inference) and data analytics (e.g. deep learning), to enable non-linear and non-Gaussian system modeling. The research addresses four fundamental questions : 1) how to quantify the accuracy and confidence in system tracking and performance variation prediction, given the limited observability; 2) how to incorporate uncertainty arising from varying operating conditions and environmental disturbance into prognostic models, 3) how to effectively fuse different data analytic techniques into one prognostic model, in order to take advantage of the strength of each of the techniques; and 4) how to improve the computational efficiency of the modeling and prediction process by leveraging emerging infrastructure enabled by cloud computing, for on-line, real-time, and remote prognosis.
Specific research tasks include: 1) deriving system health indicators as a function of operating conditions measured by sensors through machine learning techniques (e.g. deep leaning); 2) developing stochastic models of system degradation based on multi-mode particle filter with adaptive resampling capability, to track variations in health indicators for prediction of performance deterioration with time-varying degradation rates and/or modes; 3) developing an optimization method to detect transient changes in health indicators due to abrupt fault occurrence; and 4) improving computational efficiency of particle filtering by modifying the sampling and resampling strategies and leveraging cloud-based computing.
This research contributes to the fundamental theories of state estimation, tracking, prediction, and uncertainty evaluation. These theories can provide guidance to decision-making in maintenance policy of a wide range of dynamical systems, e.g., RUL prediction for aircraft engines for improved spare part management, fault diagnosis of HVAC systems in residential and commercial buildings, tool wear prediction in machine tools for intelligent manufacturing, etc.