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

Spooner, MaxDTU Compute, Technical University of Denmark, Kgs. Lyngby, Denmark (författare)

Validerad;2018;Nivå 2;2018-11-07 (johcin)
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.

Kulahci, MuratLuleå tekniska universitet,Industriell Ekonomi,DTU Compute, Technical University of Denmark, Kgs. Lyngby, Denmark(Swepub:ltu)murkul (författare)
DTU Compute, Technical University of Denmark, Kgs. Lyngby, DenmarkIndustriell Ekonomi (creator_code:org_t)

Ingår i:Chemometrics and Intelligent Laboratory Systems: Elsevier183, s. 102-1120169-74391873-3239

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