SMS Group adds AI-based predictive maintenance to product portfolio
22/05/2020
SMS Group and Semiotic Labs, a scale-up company based in Leiden, the Netherlands, have signed an agreement to cooperate in the field of predictive maintenance.The artificial intelligence (AI)-based technology developed by Semiotic Labs uses electrical signals and the data fingerprint of AC motors and other rotating equipment to monitor and analyse the condition of critical plant assets and enable early and reliable prediction of developing faults.
In contrast to traditional vibration-based solutions, the SAM4, developed by Semiotic Labs, operates based on sensors installed directly in the control cabinet, not on the asset itself. This solution is particularly useful for the in-service monitoring of equipment under rough operating conditions, as is typical in the metallurgical industry.
The SAM4 has been successfully implemented on numerous wide hot strip mills and other applications in steel plants throughout Europe. The convincing results achieved by the SAM4 under such highly demanding in-service conditions and tests at the SMS Group workshops led to the decision to make this technology part of the SMS Group product portfolio.
“As part of the agreement with Semiotic Labs, the SAM4 will be integrated as an app in the MySMS platform,” explained Dr Eike Permin, Chief Operating Officer, SMS Digital. Furthermore, the integration of the SAM4 into Genius CM, SMS Group’s condition monitoring system, is also planned, as well as cooperation in the field of data analyses and joint development activities between the two companies.
The cooperation will become another important element of SMS Group’s strategy of supplying smart maintenance solutions that help customers to maximise uptime. Thus, strategic predictive maintenance that is based on condition monitoring will become much more reliable and efficient than maintenance strategies that are based on operating times. In addition, it will increase the lifetime of components and the overall efficiency of equipment.
