Fault detection for batch monitoring and discrete wavelet transforms

Fang Li, George Church, Mani Janakiram, Howard Gholston, George Runger

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)999-1008
Number of pages10
JournalQuality and Reliability Engineering International
Issue number8
StatePublished - Dec 2011


  • 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


Dive into the research topics of 'Fault detection for batch monitoring and discrete wavelet transforms'. Together they form a unique fingerprint.

Cite this