JMP Webinar: Fault Detection and Diagnosis in High Dimensional Processes Using Model Driven Multivariate Control Charts

Wednesday 17 Nov 2021, 15:00 → 16:00 Europe/Amsterdam

Murat Caner Testik (Hacettepe University)

Fault Detection and Diagnosis in High Dimensional Processes Using Model Driven Multivariate Control Charts

Speakers: Jianfeng Ding, Sr. Research Statistician Developer, Jianfeng.Ding@jmp.com ; Jeremy Ash, Analytics Software Tester, Jeremy.Ash@jmp.com

Moderator: Murat Caner Testik

Date: Nov. 17th 3pm CET

Bio:

Jianfeng Ding

jianfeng.ding@sas.com

Jianfeng Ding is a senior research statistician in JMP Division of SAS Institute, the most leading analytics software company in the world. She is a key research developer for JMP which creates interactive and highly visual statistical discovery software designed for scientists and engineers. In her 20 years long career at SAS Institute, she has developed and supported a wide range of statistical tools such as Multivariate, Principal Components, Factor Analysis, Partial Least Squares, Multiple Correspondence Analysis and Model Driven Multivariate Control Chart within JMP.

Ding received a bachelor’s degree in Meteorology from Nanjing University. She earned a master’s degree in marine, earth and atmospheric sciences, and a master’s degree in statistics from North Carolina State University.

 

Jeremy Ash

jeremy.ash@jmp.com

Dr. Jeremy Ash is an Analytics Software Tester in the JMP Division of SAS Institute. He evaluates statistics methodology in the JMP software with a particular focus on multivariate statistics. He obtained a PhD in Bioinformatics and MS in Statistics from North Carolina State University. His research is in cheminformatics and chemometrics, and he has authored several publications on development of software and statistical methodology in the area.

1 Fault Detection and Diagnosis in High Dimensional Processes Using Model Driven Multivariate Control Charts

The Model Driven Multivariate Control Chart (MDMCC) is a new platform in JMP that allows users to efficiently monitor many correlated process variables. The platform can interface with the PCA and PLS platforms to monitor multivariate process variation over time, give advanced warnings of process shifts, and suggest probable causes of process changes. We will demonstrate several use cases. First, we demonstrate fault diagnosis in an offline setting with an example from polyethylene manufacturing. Offline diagnosis often involves switching between many multivariate control charts, univariate control charts, and diagnostic plots. MDMCC provides a user-friendly way to move between these plots. Next, we demonstrate online monitoring of a PLS model using a simulator of a real world industrial chemical process — the Tennessee Eastman Process. We demonstrate how MDMCC can perform online monitoring by connecting JMP to an external database. Measuring product quality variables often involves a time delay before measurements are available, which can delay fault detection substantially. When MDMCC monitors a PLS model, the variation of product quality variables is monitored as a function of process variables. Since process variables are often more readily available, this can aide in the early detection of faults.

Speakers: Jeremy Ash (JMP), Jianfeng Ding (JMP)