Use of data analysis for process performance enhancement
Mathematical background: Sources and characterization of errors in data,
Random variables, probability density functions, estimation, confidence
intervals and hypothesis testing, stochastic signals, frequency domain
analysis of signals, measures of nongaussianity.
Process Modeling: Model structures, linear regression, Nonlinear
regression, principla Component Analysis, Independent Component Analysis.
Applications: Parameter estimation in linear and nonlinear processes,
Data Reconcilation, Continuous/Batch process monitoring using MSPC,
controller performance monitoring, fault diagonis, chemometrics,
biomedical and speedh signal processing.
- Teacher: naras Shankar Narasimhan S