System self-assessment of survival in time series modeling

The concept, theoretical argument, and practical implementation of system self-assessment of survival using time series modeling is defined , investigated, and developed. System self-assessment of survival predicts conditional reliability for a future period of time or usage, to support an operational mission in real-time. As implemented, performance measures are monitored and modeled in physical terms, then associated models are developed in probability/statistical terms. The key issues in system self-assessment of survival are physical performance measurement and related modeling, forecasting, and survival estimation.

The research develops theoretical connections between physical performance assessment and existing time series modeling, yielding a self-assessment of survival model, based on the concept of performance reliability. Different methods, including Autoregressive Integrated Moving Average (ARIMA), exponential smoothing, and realtime recurrent neural networks, are assessed regarding modeling and prediction capabilities in real-time. In order to meet the real-time requirements of self-assessment of survival, model "self-generation" is emphasized in the context of on-line performance observation. For demonstration and validation, the research work develops the framework of a deliverable software package, Real-Time System Self-Assessment of Survival (RTSAS), which performs real-time data acquisition and survival selfassessment.

The research describes methods useful for system self-assessment of survival based on physical system performance measures and time series modeling in both single failure mode and multiple, independent, failure modes. Results produced in linear trend exponential smoothing show promise for field real-time applications, provided resolution of physical signals can be obtained and the failure mode is properly defined in terms of physical performance.