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.
REFERENCES(11)
1.
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.
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.
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.
NETZER M., MICHELBERGER J., FLEISCHER J., 2020, Intelligent Anomaly Detection of Machine Tools Based on Mean Shift Clustering, Procedia CIRP, 93, 1448–1453.
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