Multivariate Control Chart Pattern Recognition Model using Support Vector Machine

By : Khimya Tinani , Sarah Pathan , Megha Sikawat

Page No: 98-124

Abstract:
Statistical process control (SPC) is essential for preserving quality and reducing variation in production and service operations. SPC relies on control charts, which graphically show process performance over time. However, interpreting these charts can be challenging, particularly when trends are overlooked. Then, pattern recognition (PR) enhances and automates SPC analysis by identifying non-random patterns that indicate specific problems or different control chart patterns (CCPs) seen in the process behavior. Multivariate statistical process control (MSPC) is an advanced extension of traditional SPC that monitors multiple correlated variables simultaneously, providing a comprehensive view of process stability. This paper presents a multivariate control chart pattern recognition model using support vector machine (MCCPR-SVM) to recognise control chart patterns (CCPs) in multivariate process. Support vector machine (SVM) was chosen for its effectiveness in handling high-dimensional data and distinguishing CCPs in multivariate process. Synthetic data was generated by taking three variables for normal (NR), increasing trend (IT), decreasing trend (DT), systematic (SY) and cyclic (CY) patterns. The model was evaluated using confusion matrices and it has been observed that the model exhibits high performance and overall accuracy of the model is 97.2%.

Authors
KHIMYA TINANI  Department of Statistics, Sardar Patel University, Vallabh Vidyanagar, Gujarat, 
SARAH PATHAN  Department of Statistics, Sardar Patel University, Vallabh Vidyanagar, Gujarat, 
MEGHA SIKAWAT Department of Statistics, Sardar Patel University, Vallabh Vidyanagar, Gujarat,
 

DOI: DOI-https://doi.org/10.32381/IAPQRT.2026.50.01.5