An assessment of the kernel‐distance‐based multivariate control chart through an industrial application

Walid Gani · Hassen Taleb · Mohamed Limam

29 citations0/0 files storedQuality and Reliability Engineering International

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Advanced Statistical Process Monitoring Statistical Methods and Applications Fault Detection and Control Systems

Abstract

Abstract Traditional multivariate quality control charts assume that quality characteristics follow a multivariate normal distribution. However, in many industrial applications the process distribution is not known, implying the need to construct a flexible control chart appropriate for real applications. A promising approach is to use support vector machines in statistical process control. This paper focuses on the application of the ‘kernel‐distance‐based multivariate control chart’, also known as the ‘k‐chart’, to a real industrial process, and its assessment by comparing it to Hotelling's T2 control chart, based on the number of out‐of‐control observations and on the Average Run Length. The industrial application showed that the k‐chart is sensitive to small shifts in mean vector and outperforms the T2 control chart in terms of Average Run Length. Copyright © 2010 John Wiley & Sons, Ltd.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 29 citations exist upstream.

References

2005Introduction to Statistical Quality Control 2003A kernel-distance-based multivariate control chart using support vector methods 2002Support vector machines for recognizing shifts in correlated and other manufacturing processes 2001A Smooth Nonparametric Approach to Multivariate Process Capability 2005Kernel distance-based robust support vector methods and its application in developing a robust K-chart 2006Multivariate Fuzzy Multinomial Control Charts

Cited by

2019One‐sided control chart based on support vector machines with differential evolution algorithm 2011Optimal T2 Control Chart with a Double Sampling Scheme – An Alternative to the MEWMA Chart 2017Time-adaptive support vector data description for nonstationary process monitoring 2014A Comparison Study of Distribution‐Free Multivariate SPC Methods for Multimode Data 2012Performance Evaluation of One‐Class Classification‐based Control Charts through an Industrial Application 2018A control scheme for monitoring process covariance matrices with more variables than observations 2018

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.