Durch miFLEETS können wir Überbestände weiter reduzieren und so eine mögliche Verschrottung am Ende des Produktlebenszyklus vermeiden. Gleichzeitig stellen wir die Versorgung des Marktes mit Ersatzteilen sicher, was die Lebensdauer von Fahrzeugen erhöht. Beides trägt zu einer effizienteren Ressourcennutzung bei und hilft unsere Umwelt zu schonen.

From the press release: “The team identified good practices and good performances that will be shared with the nuclear industry globally, including […] a machine learning software to monitor turbine performance”.

SSAB has a leading position in high-strength steels and related services. SSAB’s Hämeenlinna steelworks produces advanced high-strength galvanised steel for customers in the automotive industry.[…] By optimising the production process, the Hämeenlinna steelworks wanted to minimise the number and duration of disturbances across the galvanising line and to ensure the highest product quality. This was accomplished through the Caverion Intelligence Anomaly Detection service, which uses existing process data in combination with advanced machine learning for early detection of anomalies across the whole process.

“We have integrated machine learning as a part of our reliability engineering process. The results we have achieved are very encouraging and we are expanding the use of this capability in various parts of our processes. Caverion has been an excellent partner for us on this journey. They have a strong, mature and proven service concept and a very competent team. The service is constantly evolving to meet our evolving needs.”

Stockholm’s energy company Stockholm Exergi has been using the Caverion Intelligence Anomaly Detection Solution to monitor its heat and power generation in the greater Stockholm area since 2019. The system supports operators and maintenance personnel and ensures reliable production performance. It also prevents unplanned downtime by detecting anomalies at an early stage. The system does this by continuously analysing thousands of signals and using advanced machine learning algorithms to recognise deviations. By identifying deviations early, errors can be corrected in good time and the risk of downtime in production can be avoided.

“By using the Caverion Intelligence AI solution we have been able to detect and mitigate several deviations long before they escalated into real failures.“