Monitoring batch processes with dynamic time warping and k-nearest neighbours

Max Spooner*, Murat Kulahci

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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A novel data driven approach to batch process monitoring is presented, which combines the k-Nearest Neighbour rule with the dynamic time warping (DTW) distance. This online method (DTW-NN) calculates the DTW distance between an ongoing batch, and each batch in a reference database of batches produced under normal operating conditions (NOC). The sum of the k smallest DTW distances is monitored. If a fault occurs in the ongoing batch, then this distance increases and an alarm is generated. The monitoring statistic is easy to interpret, being a direct measure of similarity of the ongoing batch to its nearest NOC predecessors and the method makes no distributional assumptions regarding normal operating conditions. DTW-NN is applied to four extensive datasets from simulated batch production of penicillin, and tested on a wide variety of fault types, magnitudes and onset times. Performance of DTW-NN is contrasted with a benchmark multiway PCA approach, and DTW-NN is shown to perform particularly well when there is clustering of batches under NOC.

Original languageEnglish
JournalChemometrics and intelligent laboratory systems
Pages (from-to)102-112
Publication statusPublished - 15 Dec 2018


  • Batch process
  • Dynamic time warping
  • Nearest neighbours
  • Pensim


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