Wärtsilä’s Data-Driven Dynamic Maintenance Planning solution optimises engine maintenance
The technology group Wärtsilä introduces its Data-Driven Dynamic Maintenance Planning (Data-Driven DMP) solution. This latest developed feature enhances the company’s existing Dynamic Maintenance Planning solution by utilising digitalisation and Wärtsilä’s extensive capabilities in analytics. It is an integral part of the company’s Lifecycle Solutions value proposition, and adds further customer benefits and value not possible with conventional DMP solutions.
Data-Driven DMP increases the uptime of assets and reduces lifecycle costs by optimising major overhaul intervals, without compromising operational reliability or engine efficiency.
Furthermore, assets are maintained in prime condition without the need for on-site visual inspections performed by Wärtsilä personnel, since the solution’s digital capabilities reduce the need for such on-site visits. Customer personnel can be trained to take fluid samples and to carry out borescope inspections of engines with remote support from experienced and specialised experts in Wärtsilä’s Expertise Centres.
Leveraging the new capabilities and keeping the engines in prime condition eliminates the need for the usual opening inspection of the engine.
“Data-Driven DMP adds a new dimension to our Lifecycle Solutions’ value proposition, delivering greater flexibility and increased cost savings for the customer. Existing DMP contracts can be changed to take advantage of the extra benefits provided by Data-Driven DMP, should customers wish,” says Frank Velthuis, Director Digital Product Development, Wärtsilä Marine.
The technology for Data-Driven DMP is supported by Wärtsilä’s Expert Insight product, which enables proactive and rapid advice and assistance to be delivered to customers from the Expertise Centres. Expert Insight has been credited with taking predictive maintenance to the next level.
Wärtsilä’s Lifecycle Solutions offering is backed by a combination of the company’s extensive in-house know-how along with AI and predictive analytics. It takes a holistic approach to ensure sustainable and reliable operational performance.
