Simulation for data-driven sensor delay estimation in industrial processes using multivariate projection methods

Published: 16 January 2024| Version 4 | DOI: 10.17632/32hv69mnj6.4
Tim Offermans


Source code and results for exactly regenerating the data and results of the simulation study reported in (to be published). Industrial sensory data is simulated and subjected to several data-driven methods for estimating temporal delays between the sensors. A new method is proposed to estimate these delays by optimizing multivariate correlations, and is shown to be more accurate than the currently more standard method that optimizes bivariate correlations. Updated on November 6th, 2023 after internal review by authors.


Steps to reproduce

Unzipping '' and running the script 'simulation.m' in the subfolder 'simulation' should reproduce the results.


Radboud Universiteit, Radboud Universiteit Institute for Molecules and Materials


Multivariate Analysis, Chemometrics, Industrial Process, Multivariate Statistical Process Control, Statistical Analysis