Fraunhofer Institute for Industrial Mathematics ITWMIn the »Re(Pro)³« project, we combine Condition Monitoring and Predictive Maintenance with automated quality monitoring. In this way, we help companies to detect errors in production at an earlier stage – both in the product itself and in the manufacturing process, in order to significantly reduce reject rates through the digitalization of production processes. The core idea of Re(Pro)³ is to combine methods from image processing and Condition Monitoring in order to develop an image data-based system that integrates temporal changes in product qualities and process variables caused by wear, defects or misconfiguration. The aim is to sustainably reduce avoidable rejects in automated production processes.
Re(Pro)³ – Ressourcenoptimierte Produktion durch Inline Prozess- und Produktüberwachung: Effiziente Produktionsanlagen durch bilddatenbasiertes Condition Monitoring und Predictive Maintenance
Re(Pro)³: Image Data-Based Solutions for Production Processes
At the heart of the project is an image data-based system that uses Image Processing and Machine Learning to integrate temporal changes in product qualities and process variables.
© freepik / Fraunhofer ITWM
Efficient Production Systems Thanks to Image-Based Condition Monitoring and Predictive Maintenance
Re(Pro)³ – Resource-Optimized Production Through Inline Process and Product Monitoring
Classic Condition Monitoring keeps an eye on the status of machines, plants or production systems during operation. It is based on the analysis of measured values (e.g. temperature, vibrations, pressure, acoustic signals) obtained from sensors or imaging systems.
Objectives of Condition Monitoring:
- early detection of anomalies that indicate wear or potential faults
- avoiding sudden breakdowns through precise knowledge of the current condition of the system
- optimize operating performance by adjusting system parameters
Predictive Maintenance goes beyond Condition Monitoring by using data analysis, algorithms (e.g. Machine Learning) and predictive models to make forecasts about the future condition of a system. It determines the optimum time for maintenance before a failure occurs.
Objectives of Predictive Maintenance:
- maximizing the service life of machines through targeted interventions
- carrying out maintenance measures as late as possible and as early as necessary
- minimizing downtimes and costs
In this way, we avoid unnecessary downtime, as is the case with purely time-based maintenance, and increase the safety and efficiency of the systems. Production quality is improved by avoiding faulty components.
Automated Surface Inspection for Flawless Products Including Changes Over Time
Automated surface inspection systems monitor the quality of products using imaging processes. These systems detect and classify defects on surfaces. Defective products are then sorted out or reworked. However, further analysis, e.g. of the development over time, does not currently take place. However, in addition to operating and control errors, the cause of defective products often lies in the wear of individual components of the production system. By observing how product quality changes over time, it is possible to predict at an early stage when fault tolerances will be exceeded and maintenance of the production system will be required.
By analyzing defect patterns, which are determined in advance based on virtual inspection planning, and combining them with other measurement data from the production plant, the causes of defects can be identified more precisely and the development of defects over time can be predicted. On this basis, targeted measures can be taken, such as adapting the system control through intelligent control or the timely replacement of certain components that are likely to fail. As a result, fewer faulty products are produced, downtimes in the production process are reduced and the sustainability of the entire production process is significantly increased.
Pooling Expertise and Outlook in Application
We develop these processes by pooling the expertise of:
- the department »Image Processing« with its expertise in surface inspection processes and virtual inspection planning
- the department »System Analysis, Prognosis and Control«, which contributes its knowledge in the fields of Condition Monitoring, Predictive Maintenance and System Control.
The result is a software framework for the sustainable, resource-optimized operation of production facilities using image-based processes. We see future applications in various companies such as the textile industry, the plastics and metal industry, the packaging industry or the building materials industry (e.g. wall cladding made of fiber materials).
Project Duration and Funding
The Rhineland-Palatinate Ministry of Science and Health is funding Re(Pro)³ with almost €800,000. The project will run for four months (from November 1, 2024 to February 28, 2025).