Batch operations are encountered in many industries and measurements are often recorded from automated sensors. It is important to determine whether an unknown batch is normal or unusual given a set of reference batches from normal operations. The measurements from a single batch can contain temporal readings that comprise a large time series. A discrete wavelet transformation (DWT) is applied to use the time and frequency localization of wavelets to extract features. A large number of coefficients can result and several methods to create summary features from the denoised coefficients obtained from DWT are compared. Also, a new summary feature incorporates information from denoised wavelet coefficients. The proposed study considers discrete wavelet- decompositions combined with principal component analyses to summarize batch characteristics. Results were validated on an industry data set.
- multivariate control chart
- principal component analysis
- statistical process control
- time series
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Management Science and Operations Research