DFusion - Disturbance Data Fusion

Purpose and goal:

Nearly 50% of Swedish production capacity is not used due to disturbances caused by e.g. equipment failures, process deviations, quality issues, or wrong handling. Disturbances also increase process waste, need for raw material and energy, and impair the work environment. The project’s purpose is to support companies in managing disturbances, to create efficient and sustainable production systems.

The goal is to explore and develop digital tools for data fusion and analysis of industry data with the goal of enhancing disturbance handling, increase efficiency and decrease waste. Expected results and effects:

Expected effects are improved solutions for disturbance handling, increasing efficiency in production systems, as well as improved environmental and social sustainability. The main results from the project are knowledge, work methodology, technical solutions, and demonstrations of how different data sources can be combined (data fusion) to yield more performant solutions for handling disturbances by strengthening categorization, prioritization, and analytics.

Results also include publications, education materials and implemented solutions in partner companies. Approach and implementation:

RISE and Chalmers University will conduct research on data fusion based on three industrial case studies at Nolato Gota, Orkla Foods, and Nexans. Solution providers Colmeo, Good Solutions, IFM Electronic, and Qestio will making sure the studies include two or more data sources, e.g. combining disturbance logs with video streams, sensor data and maintenance system data.

The fused data will be used to develop analytics tools for visualization, prioritization, categorization recommendation, powered by state-of-the-art data analytics, machine learning (ML) and AI technologies.