Application of grey model for machine degradation prognostics
Predicting machine degradation before final failure occurs is very important. This paper presents a method to predict the future state of machine degradation based on grey model and one-step-ahead forecasting technique. Specifically, the feasibility of grey model as a predictor for machine degradation prognostics system has been investigated. Grey model GM(1,1) has employed to forecast the future state of machine degradation, but the result is not satisfactory. Finally, a modification of GM(1,1) has made to improve the accuracy of prediction. However the model was built by using only four input data, it is able to track closely the sudden change of machine degradation condition. Real trending data of low methane compressor acquired from condition monitoring routine are employed for evaluating the proposed method.
By Tony Martini, Economics ::: Advisor: Michael Jones ::: Presentation ID: AM_D39 ::: Abstract: This study suggests new methods of measuring the effectiveness of universities in protecting students from the impact of automation on the labor market. Using US government data on occupational activities and traditional measures of university outcomes, we produce a ranking of college majors by their susceptibility to automation and determine correlations between predicted traditional and automation-based outcomes. These are combined into a complete metric for the "automation resistance" of a university. Using an approach centered on distinctly human skills, occupational activities can be ranked by their automation resistance. Institutions which develop student skills with a focus on automation resistance should likewise demonstrate greater long-term viability. We demonstrate this methodology through a case study using data from the University of Cincinnati.