What is the production status of a machine? Does the quality of the end products remain constant? When data is expertly collected and used, it can assist in many areas. For example, monitoring production or the condition of wear parts.
„These are the core competencies of OSDi. We are developing various software solutions that use data to identify and leverage efficiency potential in the production of solid formulations, for example in the area of maintenance,“ explains Sven Vulp, Program Owner dynamic data.
Flexibility and dynamics
With the help of data and OSDi’s software solutions, users can design their maintenance strategies more efficiently – and react before failures or quality losses occur. In this way, they are moving away from a fixed-time maintenance strategy, i.e. maintenance that always takes place at a fixed time at regular intervals. Instead, they are developing a condition-based maintenance strategy that is based on the actual condition of the machine and even predicts and incorporates AI-supported wear and tear.
Condition monitoring: What is the condition of the machine?
How does maintenance based on data work? The OSDi tool ConditionMonitor collects data from production machines and compiles the most important parameters in an intuitive and comprehensive dashboard. This gives users a precise overview of the real-time status and warnings of their machines. Frequent errors are easy to analyse and extract to start root cause analysis in a focused way. In addition, the tool compares the current data with historical measured values. This way, it prepares for deviation analysis at an early stage and helps to avoid downtime. “The ConditionMonitor is currently available as a minimum viable product. We are testing it together with our customers and development is ongoing,” reveals Sven Vulp.
What information is hidden in the data that is available? OSDi’s PerformanceManager tool acts as an extension of ConditionMonitor, providing users with recommended actions and settings based on the data collected. “PerformanceManager compares real-time data with historical data. Our experts analyze this and enrich it with their own empirical values to obtain a reliable data basis for production optimization,” says Sven Vulp. The application actively points out potential for performance improvement to users and supports them in the form of recommendations for action – delivered in OSDi’s recently launched online learning app alva. Currently, the PerformanceManager is in the prototyping phase and is being further developed.
Predicting wear and tear with AI
The PredictiveMaintenance application goes one step further and uses self-learning technology to determine wear before it occurs. “The tool analyzes data from the past and the current batch. In this way, it helps establish more efficient and individual maintenance strategies,” explains Sven Vulp. The application calculates the optimal time to clean, maintain or replace components and reduces unplanned downtimes. The OSDi team has already conducted extensive tests to identify the wear levels of core components and created first algorithms. The knowledge gained is currently being validated and optimized with pilot customers.
“Data can increase the efficiency of solid formula production in so many areas. Our digital tools enable our customers to exploit this potential,” is how Sven Vulp summarizes the work undertaken by the digital unit.