Wes Hines

Abstract:

Energy system on-line-monitoring is becoming a crucial component of improving safety, reliability, and profitability. The Holy Grail is the development prognostic methodologies to accurately predict the Remaining Useful Life (RUL) of a system or component for predictive maintenance and effective risk mitigation. Calculating precise RUL estimates requires both system specific maintenance information and performance data to develop representative lifecycle models. Current conventional prognostic methods focus on process data and do not utilize maintenance data to directly influence the modeling and data analysis. However, equipment maintenance impacts future system degradation and is dependent on the maintenance actions taken. Differences in the amount of degradation removed from a system are common for repaired equipment compared to replacements. This talk discusses methods of incorporating maintenance information into Lifecycle Prognostics and the effect it has on prediction error and uncertainty compared to maintenance independent models. <br />Conventional Lifecycle Prognostics is a term used when the RUL is seamlessly predicted from beginning of component life (BOL) to end of component life (EOL). When a component is put into use, the only information available may be past failure times, and the predicted failure distribution can be estimated with reliability methods such as Weibull Analysis (Type I). As the component operates, it begins to consume its available life. This life consumption may be a function of system stresses, and the failure distribution should be updated (Type II). When degradation becomes apparent, this information can be used to again improve the failure distribution estimate (Type III) of the specific component. <br />The results of integrating past maintenance information into conventional lifecycle prognostics indicate that maintenance specific models produce significantly lower prediction error and model uncertainty. This serves as a proof of concept for investigation into more effective ways to utilize maintenance data in prognostic modeling, while also emphasizing the importance of digital maintenance records in industry.