Reduction of random variables in the Stochastic Harmonic Function representation via spectrum-relative dependent random frequencies

authored by: Jianbing Chen, Liam Comerford, Yongbo Peng, Michael Beer, Jie Li
Abstract: Two significant developments pertaining to the application of the Stochastic Harmonic Function representation of stochastic processes are presented. Together, they allow for Gaussian records to be simulated within the bounds of the representation with the fewest number of random variables. Specifically, independent random frequencies that form a staple component of the Stochastic Harmonic Function are replaced by dependent random frequencies, along with a specific scheme for choosing frequency interval widths. Numerical examples demonstrating spectrum reconstruction accuracy and estimated PDF convergence to the Gaussian are presented to support the work.
Organisation(s): Institute for Risk and Reliability
External Organisation(s): Tongji University
University of Liverpool
Type: Article
Journal: Mechanical Systems and Signal Processing
Volume: 141
ISSN: 0888-3270
Publication date: 07.2020
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Control and Systems Engineering, Signal Processing, Civil and Structural Engineering, Aerospace Engineering, Mechanical Engineering, Computer Science Applications
Electronic version(s): https://doi.org/10.1016/j.ymssp.2020.106718 (Access: Closed)