SKF is a world leader in rolling bearings and related technologies. As a prominent industrial player, the work towards more sustainable operations is constant and ambitious. SKF has developed the RecondOil Box, which removes nanoparticle contaminants and continuously restores oil lubricants to a “like new” state of cleanliness to facilitate the reuse of industrial oils. To optimize the predictive maintenance of the oil filters in the application, SKF now seeks solutions to monitor the condition remotely using sensors rather than having to process oil samples at laboratory facilities.
The RecondOil Box is a plug-and-play product based on novel chemistry-based cleaning and world-class depth filtration. To maintain the solution’s performance, SKF has established a condition-based monitoring service based on laboratory tests to support maintenance planning of the filters inside the box. Pain points for the industrial customers that use the product include the time-consuming process of taking oil samples and sending them to an external laboratory for testing. Traditionally, filter inserts are replaced at fixed intervals, but this approach may not be the most effective since conditions can be changeable.
Developing a proactive maintenance approach that uses real-time data to identify the maintenance needs of the RecondOil Box would simplify and scale the process, thus encouraging adoption and enabling greater reuse of industrial lubricant oils. Knowing ahead of time when the filter needs replacement would optimize the effect of the product and help reduce costs. Different parameters in the oil can be analyzed to indicate that the filter should be changed, such as particle count, TAN/TBN value, pressure drop over the filter insert, water content in oil, and FTIR spectra, amongst other parameters.
The unique reconditioning technology of the RecondOil Box extends the life and desirable properties of the oil, closing the loop on the industrial use of lubrication oil. By incorporating top-of-the-line online monitoring and AI tools for conditional analysis, the solution could be scaled faster by making it easier for customers to use.
Summary of requested solution
The ideal solution should incorporate the latest sensor technology into a continuous monitoring system that detects when the RecondOil Box needs attention and triggers actions. It could be constructed using an AI algorithm to analyze data from multiple sensors and filter out noise, thereby predicting filter health through trend analysis. Additionally, any sensor must be simple, cost-effective, and durable. Furthermore, the solution should preferably operate without extensive calibration before commissioning.
Your opportunity of SKF
SKF is the world’s largest bearing manufacturer, with 17,000 distributors in 130 countries. As an industry leader, they offer outstanding global potential for scaling your company’s solution. SKF constantly looks for innovative solution providers who can help make their offerings even more effective and decrease their carbon footprint. This opportunity is an excellent chance to share industry knowledge and provide you with valuable learnings, opening up further opportunities within the industry while supporting our customers on their sustainability journey.
Examples we're looking for
Real-time monitoring sensors
A sensor system that can continuously gather data on relevant parameters in the oil content, such as reservoir size, contamination- and water level, would help provide accurate insights into the condition of the filters. Other parameters include particle count, TAN/TBN value, pressure drop over the filter insert, water content in oil, and FTIR spectra. Sensor solutions that require calibration to fit a specific application/oil become problematic as the unit must be plugged and played.
AI-powered predictive maintenance solutions
Similarly, algorithms that can accurately and reliably detect the patterns and anomalies in the sensor data automatically determine when the filter needs attention and trigger actions accordingly would allow for a proactive maintenance approach, optimizing the effectiveness of the application and reducing unnecessary filter replacements.
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