| Abstract | The core of PHM (Prognostic and Health Monitoring) technology is prognostics which is able to estimate time to failure (TTF) for the monitored components or systems using the built-in predictive models. However the development of predictive models for TTF estimation remains a challenge. To address this issue, we proposed to develop machine learning-based models for TTF estimation by using the techniques from machine learning and data mining. In the past decade, we have been working on the development of machine learning-based models for estimating TTF and applied the developed technology to various real-world applications such as train wheel prognostics, and aircraft engine prognostics. In this paper, we report two kinds of machine learning-based models for estimating TTF, including multistage classification, on-demand regression. The multistage classification improves the TTF estimation over one stage classification by dividing the time window into more small narrow time windows. A case study, APU prognostics, demonstrates the usefulness of the developed methods. The results from the case study show that the machine learning-based modeling method is an effective and feasible way to develop predictive models to estimate TTF for PHM. |
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