Automatic Time Series Segmentation as the Basis for Unsupervised, Non-Intrusive Load Monitoring of Machine Tools

• Main Authors: Seevers, J-P., Johst, J., Weiß, T., Meschede, H., Hesselbach, J.
• Format: Conference Proceeding
• Language: English
• Subjects: energy management; intelligent sensor; load profile analysis; operational state identification; sustainable manufacturing; unsupervised learning
• ISSN: 2212-8271; 2212-8271
• DOI: 10.1016/j.procir.2019.03.178

Detailed energy monitoring and benchmarking at the individual component level is necessary to increase energy efficiency in complex production systems. Non-intrusive load monitoring (NILM) provides an economical solution for operational state detection and load disaggregation without the need for large-scale use of fine-grained energy meters. Existing supervised NILM approaches require detailed training data including control information about individual devices. Unsupervised approaches, on the other hand, often require high measurement resolution and are faced with the problem of detecting continuous states. This paper proposes a simple step-by-step, completely unsupervised NILM approach that distinguishes between almost constant and non-constant segments with flexible segment lengths. Taking into account various electrical parameters and their statistical moments, hierarchical density-based spatial clustering of applications with noise (HDBScan) is applied to constant segments. The analysis of non-constant segments is based on agglomerative hierarchical clustering and dynamic time warping. Based on real energy monitoring from a gear manufacturing system we show the applicability of our methodology and discuss how it can be combined with existing NILM techniques.