Remaining life prediction of cores based on data-driven and physical modeling methods

Abstract

This chapter presents development of enabling technologies that are able to assess the reliability of remanufactured products based on predictive modeling methods, to describe fast and accurate prediction algorithms that are able to predict condition of critical components or parts of manufactured products based on historical data. Machine health condition prediction of critical components under the situation of insufficient data, missing prior fault knowledge, and noisy measurement are studied using an enhanced online sequential learning-fuzzy neural network. Meanwhile, Weibull model-based reliability analysis is investigated in this chapter. Performance of various Weibull parameter estimation methods is compared using case studies. Results of this part of research have enabled the development of a product reliability analysis tool that is able to characterize the product failure modes, failure rate, and reliability profile.