Modeling pitting growth data and predicting degradation trend

A non-statistical modeling approach to predict material degradation is presented in this paper. In this approach, the original data series is processed using Accumulated Generating Operation (AGO). With the aid of the AGO which weakens the random fluctuation embedded in the data series, an approximately exponential curve is established. The generated data series described by the exponential curve is then modeled by a differential equation. The coefficients of the differential equation can be deduced by approximate difference formula based on least-squares algorithm. By solving the differential equation and processing an inverse AGO, a predictive model can be obtained. As this approach is not established on the basis of statistics, the prediction can be performed with a limited amount of data. Implementation of this approach is demonstrated by predicting the pitting growth rate in specimens and wear trend in steam, generator tubes. The analysis results indicate that this approach provides a powerful tool with reasonable precision to predict material degradation.