Abstract
For fitting the failure data with non-steady growth trend better, a new method is proposed to establish combinational models by combining two NHPP models, which have better fitting effects in four candidate models including G-O model, S-Shaped model, M-O model and Duane model for the same data. A real-valued genetic algorithm is used to calculate the maximum likelihood estimates of the parameters in combinational models for solving the problem that the traditional maximum likelihood estimation algorithms cannot give parameter estimates when fitting some data sets. Then, five published failure data sets, most of which have non-steady growth trend, are selected to verify the effectiveness of our models. The results show that the MSE of our models are always lower. It reduces at least 3.12%, and at most 69.31% compared with the single optimal model. These indicate that our method is feasible and effective.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
