Predictive Maintenance for leading Manufacturer of Industrial Turbines

Case Study

Predictive Maintenance for leading Manufacturer of Industrial Turbines

The Challenge

The customer is one of the world’s leading manufacturers of industrial turbines whose products are used for pipeline transmission, power generation, and oil & gas production. The customer decided to redefine itself from a machine-centric company to a service-centric industrial player through digital transformation, with the goal to generate new revenue streams and better help its users manage turbomachinery and site operations.

The Solution

Ness, in partnership with the customer, built 100+ web, mobile and desktop applications to support end customers in the whole lifecycle process from contract negotiation, through commissioning on site, data acquisition, machine and field monitoring, data analysis and prediction, up to condition inspection and condition-based maintenance of equipment by determining remaining useful life. The solution implemented a scalable solver capable of processing finite element analysis under different conditions. Ness also brought in Intelligent Engineering practices such as Distributed Agile best practices for software engineering and drove a new DevOps culture within the organization.

The Results

  • 2100+ units connected to the digital platform and monitored in real time
  • Increasing equipment uptime by 10 to 20% while reducing overall maintenance costs by 5 to 10% and maintenance planning time by 20 to 50%.
  • 603,000+ hours of avoided downtime