2 research outputs found
Process Knowledge Driven Change Point Detection for Automated Calibration of Discrete Event Simulation Models Using Machine Learning
Initial development and subsequent calibration of discrete event simulation
models for complex systems require accurate identification of dynamically
changing process characteristics. Existing data driven change point methods
(DD-CPD) assume changes are extraneous to the system, thus cannot utilize
available process knowledge. This work proposes a unified framework for
process-driven multi-variate change point detection (PD-CPD) by combining
change point detection models with machine learning and process-driven
simulation modeling. The PD-CPD, after initializing with DD-CPD's change
point(s), uses simulation models to generate system level outputs as
time-series data streams which are then used to train neural network models to
predict system characteristics and change points. The accuracy of the
predictive models measures the likelihood that the actual process data conforms
to the simulated change points in system characteristics. PD-CPD iteratively
optimizes change points by repeating simulation and predictive model building
steps until the set of change point(s) with the maximum likelihood is
identified. Using an emergency department case study, we show that PD-CPD
significantly improves change point detection accuracy over DD-CPD estimates
and is able to detect actual change points.Comment: This work has been submitted to the IEEE for possible publication.
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Statistical Monitoring of Covariance Matrix in Multivariate Processes: A Literature Review
Monitoring several correlated quality characteristics of a process is common
in modern manufacturing and service processes. For this purpose, control charts
have been developed to detect assignable causes before producing nonconforming
products. Although a lot of attention has been paid to monitoring the
multivariate process mean, not many control charts are available for monitoring
the covariance matrix. This paper presents a comprehensive overview of the
literature on control charts for monitoring covariance matrix in multivariate
statistical process monitoring (MSPM) framework. It classifies the research
that have previously appeared in the literature. We highlight the challenging
areas for research and provide some directions for future research