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Intelligent lining monitoring for a competetive and digitized process industry

Reference number
Coordinator RISE Research Institutes of Sweden AB - Enheten för Fiberoptik, fotonik och nano
Funding from Vinnova SEK 598 700
Project duration September 2020 - April 2021
Status Completed
Venture Strategic innovation programme for process industrial IT and automation – PiiA
Call PiiA: Digitalization of industrial value chains, spring 2020

Purpose and goal

It has been examined how advanced data analysis in combination with an innovative technology for refractory lining monitoring can add value to the process industry and be part of its digitalisation. Methods have been identified that can add value to users through anomaly detection and predictive maintenance as well as through automatic detection of the fiber sensor layout. Methods to increase the reliability of the measurement data from the sensor have also been investigated. This has provided a basis for further development.

Expected results and effects

The results have led to an expanded consortium comprising two end users with whom a full-scale project is planned. A great customer benefit is expected from the possibility of predictive maintenance. To realize this, longer studies and more measurement and process data are needed. Automatic location of the fiber sensor can, however, be realized without further testing in an operational environment. The results also show that there is potential in using more physical modeling, which is considered easier in the short term in comparison with eg AI.

Planned approach and implementation

Based previously performed measurements, a literature study was conducted to find suitable methods for data analysis. To understand how new data analysis methods can contribute to more reliable data collection, the study was supplemented with laboratory experiments and a review and analysis of previously collected data. An analysis of the market, benefit for end users, was made from the product owner´s perspective.

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 10 June 2021

Reference number 2020-02824

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