TY - CHAP

T1 - Time Series Search and Similarity Identification for Single Item Monitoring

AU - Denkena, B.

AU - Bergmann, B.

AU - Becker, J.

AU - Stiehl, T. H.

N1 - Funding Information: Acknowledgements. The authors thank the Federal Ministry of Economics (BMWi) for its financial and organizational support of the project “IIP Ecosphere” (01MK20006A).

PY - 2022

Y1 - 2022

N2 - Monitoring process errors and tool condition in single item production is challenging, as a teach-in is not possible due to a missing reference process. An approach to this problem is anomaly detection, e.g. based on motor currents or axis position signals from metal cutting processes. However, with no references anomaly detection struggles to detect failures from signals, because failure patterns are often too similar to regular process dynamics. While single items inherently constitute an anomaly by themselves, they do contain repetitive elements, like boreholes or milled pockets. These elements are similar, what provides an anomaly detection with additional information on regular processes. Hierarchical K-Means clustering combined with Dynamic Time Warping (DTW) and Barycenter Averaging (DBA) enables the identification of similar process elements. The algorithm allows ordering similar process segments by similarity in a tree structure. The introduced method supports querying subsequences from any given cutting process, for which it returns the closest cluster in the tree. This allows to (a) improve the data basis for anomaly detection and (b) to transfer labels with errors between processes. The article demonstrates the transfer of labels (for errors) from a turning process, to a single item milling process.

AB - Monitoring process errors and tool condition in single item production is challenging, as a teach-in is not possible due to a missing reference process. An approach to this problem is anomaly detection, e.g. based on motor currents or axis position signals from metal cutting processes. However, with no references anomaly detection struggles to detect failures from signals, because failure patterns are often too similar to regular process dynamics. While single items inherently constitute an anomaly by themselves, they do contain repetitive elements, like boreholes or milled pockets. These elements are similar, what provides an anomaly detection with additional information on regular processes. Hierarchical K-Means clustering combined with Dynamic Time Warping (DTW) and Barycenter Averaging (DBA) enables the identification of similar process elements. The algorithm allows ordering similar process segments by similarity in a tree structure. The introduced method supports querying subsequences from any given cutting process, for which it returns the closest cluster in the tree. This allows to (a) improve the data basis for anomaly detection and (b) to transfer labels with errors between processes. The article demonstrates the transfer of labels (for errors) from a turning process, to a single item milling process.

KW - Barycenter averaging

KW - Machining

KW - Monitoring

KW - Pattern recognition

KW - Time series clustering

UR - http://www.scopus.com/inward/record.url?scp=85135164335&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-78424-9_53

DO - 10.1007/978-3-030-78424-9_53

M3 - Contribution to book/anthology

AN - SCOPUS:85135164335

SN - 978-3-030-78423-2

T3 - Lecture Notes in Production Engineering

SP - 479

EP - 487

BT - Lecture Notes in Production Engineering

PB - Springer Nature

ER -