System self-assessment of survival in time series modeling

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Title: System self-assessment of survival in time series modeling
Author: Lu, Huitian
Abstract: 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 .
URI: http : / /hdl .handle .net /2346 /22256
Date: 1998-05

Citation

System self-assessment of survival in time series modeling. Doctoral dissertation, Texas Tech University. Available electronically from http : / /hdl .handle .net /2346 /22256 .

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