Fault detection and diagnosis of manufacturing processes using JMP Model-Driven Multivariate Control Charts (MDMVCC)In this era of big data and complex manufacturing, it is often necessary to monitor and analyze large amounts of data generated in manufacturing processes to maintain or improve product quality. JMP provides a variety of tools to help engineers and analysts monitor large-scale processes quickly and effectively. One of them is the Model Driven Multivariate Control Chart (MDMVCC). In conjunction with the PCA and PLS platforms, MDMVCC allows users to monitor a large number of highly correlated processes to monitor multivariate process changes over time, issue advance warnings of process changes, and provide suggestions on possible causes of process changes. We will use case studies to demonstrate how to use MDMVCC to monitor and diagnose many processes for fast and efficient improvements.
Predictive Modeling and Machine Learning
