A wavelet‐based nonparametric CUSUM control chart for autocorrelated processes with applications to network surveillance. (3rd December 2018)

• Record Type: Journal Article
• Authors: Li, Jun; Jeske, Daniel R.; Zhou, Yangmei; Zhang, Xin
• Is Part Of: Quality and reliability engineering international. Volume 35:Number 2(2019)
• Page Start: 644
• Page End: 658
• Publication Date: 2018-12-03
• Subjects: long‐range dependence -- network surveillance -- nonparametric procedure -- statistical process control -- wavelets; Reliability (Engineering) -- Periodicals; Quality control -- Periodicals; High technology -- Periodicals; 620.00452
• DOI: 10.1002/qre.2427
• Languages: English
• ISSNs: 0748-8017

Abstract

Abstract: Statistical process control (SPC) has natural applications in data network surveillance. However, network data are commonly autocorrelated, which presents challenges to the basic SPC methods. Most existing SPC methods for correlated data assume parametric models to account for the correlation structure within the data. Those model assumptions can be difficult to justify in practice. In this paper, we propose a nonparametric cumulative sum (CUSUM) control chart for autocorrelated processes. In our proposed approach, we incorporate a wavelet decomposition and a nonparametric multivariate CUSUM control chart to obtain a robust procedure for autocorrelated processes without distribution assumptions. Extensive simulations show that the procedure appropriately controls the in‐control average run length and also has good sensitivity for detecting location shifts.