Abstract
Aiming at the change point recognition in the manufacturing process, this paper proposed a new detecting scheme for synchronous multiple-signal change points. The method proposed a new piecewise linear regression model to describe the change point problem, which can describe the trend of the manufacturing process rather than default the process is constant. And the group lasso constraint is used to construct the synchronous multiple-signal change points and an efficiently implemented scheme is developed to solve the model. The number and location of the change point are finally recognized by Bayesian Information Criterion (BIC). Numerical simulations verify that the proposed method has obvious advantages over existing methods. Two real cases from the body-in-white manufacturing industry and the rolling bearing degradation showed that the proposed method can be applied to the change point detection of real manufacturing process, which is conducive to the process monitoring and pattern recognition.
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.
