Similarity Search of Time Series Data: Evaluation of Search Engine in Industrial Process Data (SIFT)

Research Project, 2024 – 2026

Many manufacturing companies collect extensive production data, but improved methods for data management, analysis, and visualization are needed to maximize its value. The project aims to test and evaluate a technical solution and method in a real industrial environment to make large multivariate time series more searchable. Participating companies Nexans and Nord-Lock will use enhanced digitalization to improve productivity, reduce waste, and and improve quality.

The developed search engine enables searching in time series from numerous sensors and batches. By embedding time series and metadata into vectors stored in a vector database with similarity search functionality, anomalies and similarities can be detected. This simplifies data searches, typically challenging in large datasets. Expected outcomes include supporting industrial digitalization, reducing disruptions, increasing efficiency, enhancing quality, sustainability, and workplace conditions.