A wavelet-based distribution-free tabular CUSUM chart based on adaptive thresholding, WDFTCa is designed for rapidly detecting shifts in the mean of a high-dimensional profilewhose noise components have a continuous nonsingular multivariate distribution. First computing a discrete wavelet transform of the noise vectors for randomly sampled Phase I (in-control) profiles, WDFTCa uses a matrix-regularization method to estimate the covariance matrix of the wavelet-transformed noise vectors; then, those vectors are aggregated (batched) so that the non-overlapping batch means of the wavelet-transformed noise vectors have manageable covariances. Lower and upper in-control thresholds are computed for the resulting batch means of the wavelet-transformed noise vectors using the associated marginal Cornish-Fisher expansions that have been suitably adjusted for between-component correlations. From the thresholded batch means of the wavelet-transformed noise vectors, Hotelling's T2-type statistics are computed to set the parameters of a CUSUM procedure. To monitor shifts in the mean profile during Phase II (regular) operation, WDFTCa computes a similar Hotelling's T2-type statistic from successive thresholded batch means of the wavelet-transformed noise vectors using the in-control thresholds; thenWDFTCa applies the CUSUM procedure to the resulting T2-type statistics. Experimentation with several normal and non-normal test processes revealed that WDFTCa outperformed existing non-adaptive profile-monitoring schemes.
All Science Journal Classification (ASJC) codes
- Strategy and Management
- Management Science and Operations Research
- Industrial and Manufacturing Engineering