NYK LNG Ship Management signs optimized maintenance agreements with Wärtsilä

Written by Marine Log Staff
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With the Wärtsilä Expert Insight product, technical specialists at Wärtsilä Expertise Centers support the customer with proactive advice to avoid potential operational disturbances. [Image © Wärtsilä Corporation]

Wärtsilä has signed two long-term optimized maintenance agreements with Japan based NYK LNG Ship Management. The 15-year agreements, which took effect fom January 2021, cover the Wärtsilä 50DF dual-fuel engines and other equipment installed in two LNG carriers vessels.

The contracts were signed in December 2020 and took effect from January 2021.

The agreements are designed to maximize operational availability and ensure long-term maintenance cost predictability through by using data input to optimize all maintenance procedures.

The agreements also include Wärtsilä’s Expert Insight, a predictive maintenance product that leverages artificial intelligence (AI) and advanced diagnostics to monitor equipment and systems in real-time.

Specialists at Wärtsilä Expertise Centers are thus able to support the customer proactively to avoid potential operational disturbances, increasing the vessels’ uptime and availability.

Wärtsilä says that the technical support it will provided and the application of its digital technology will both enhance the reliability of the ships’ operations, and allow accurate budgeting of the long-term maintenance costs.

“As part of our Lifecycle Solutions approach, these agreements are designed to improve our customers’ business performance and competitiveness,” says Henrik Wilhelms, Director, Agreement Sales, Wärtsilä Marine Power. “By including Expert Insight into our offering, we are adding considerable value to the proposition since it takes predictive maintenance to a completely new level.”

The full scope of the agreements includes all scheduled parts, workshop services, field services, maintenance planning, remote operational support, Expert Insight’s asset diagnostics and anomaly detection, and dynamic maintenance planning.

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