Offline-Online pattern recognition for enabling time series anomaly detection on older NC machine tools
More details
Hide details
Institute of Production Science (wbk), Karlsruhe Institute of Technology (KIT), Germany
Markus Netzer   

Institute of Production Science (wbk), Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131, Karlsruhe, Germany
Submission date: 2020-09-30
Final revision date: 2021-01-07
Acceptance date: 2021-01-07
Online publication date: 2021-03-29
Publication date: 2021-03-29
Journal of Machine Engineering 2021;21(1):98–108
Intelligent IoT functions for increased availability, productivity and component quality offer significant added value to the industry. Unfortunately, many old machines and systems are characterized by insufficient, inconsistent IoT connectivity and heterogeneous parameter naming. Furthermore, the data is only available in unstructured form. In the following, a new approach for standardizing information models from existing plants with machine learning methods is described and an offline-online pattern recognition system for enabling anomaly detection under varying machine conditions is introduced. The system can enable the local calculation of signal thresholds that allow more granular anomaly detection than using only single indexing and aims to improve the detection of anomalous machine behaviour especially in finish machining.
ISMAIL A., TRUONG H.L., KASTNER W., 2018, Manufacturing Process Data Analysis Pipelines: a Requirements Analysis and Survey, Journal of Big Data, 6, 1–26.
GITTLER T., GONTARZ A., WEISS L., WEGENER K., 2019, A Fundamental Approach for Data Acquisition on Machine Tools as Enabler for Analytical Industrie 4.0 Applications. Procedia CIRP, 79, 586–591, DOI: 10.1016/j.procir.2019.02.088.
SOBEL W., 2014, MTConnect Standard, MTConnect Institute, Online available /standard.
BEN E., BINGYAN Z., HANSEL A., MASAHIKO M., FUJISHIMA M., 2014, Machine Monitoring System Based on MTConnect Technology, Procedia CIRP, 22, 92–97, DOI: 10.1016/j.procir.2014.07.148.
LEE J., KAO H.A., YANG S., 2014, Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment, Procedia CIRP, 16, 3–8, DOI: 10.1016/j.procir.2014.02.001.
LIN J., KEOGH E., LONARDI S., PATEL P., 2002, Finding Motifs in Time Series, Proceedings of the Second Workshop on Temporal Data Mining, 53–68.
KEOGH E., LIN J., 2005, Clustering of Time-Series Subsequence is Meaningless: Implications for Previous and Future Research, Know. Inf. Syst., 8/2, 154–177, DOI: 10.1007/s10115-004-0172-7.
SAKURAI Y., FALOUTSOS Ch., YAMAMURO M., 2007, Stream Monitoring Under the Time Warping Distance, IEEE 23rd International Conference on Data Engineering, Istanbul, 1046–1055.
EMEC S., KRÜGER J., SELIGER G., 2016, Online Fault-monitoring in Machine Tools Based on Energy Consumption Analysis and Non-Invasive Data Acquisition for Improved Resource-Efficiency, Procedia CIRP, 40, 236–243, DOI: 10.1016/j.procir.2016.01.111.
TANI GmbH Networks for Industry, Copyright 2013–2019, Tani GmbH, Nürnberg, Deutschland, Online available
NETZER M., MICHELBERGER J., FLEISCHER J., 2020, Intelligent Anomaly Detection of Machine Tools Based on Mean Shift Clustering, Procedia CIRP, 93, 1448–1453.