1. Catalogs
  2. NRG Systems
  3. TurbinePhD? Condition Monitoring System

TurbinePhD? Condition Monitoring System

TurbinePhD? Condition Monitoring System

TurbinePhD? Condition Monitoring System

Product catalog summary
Introduction
The document introduces the TurbinePhD system, a condition monitoring solution designed to enhance wind turbine operations by detecting faults early, automating diagnosis, and providing prognostic insights.
Key Features
  • Early Fault Detection: Utilizes advanced processing techniques to identify faults before they cause significant damage, reducing maintenance costs.
  • Automated Diagnosis: Minimizes false alarms and unnecessary maintenance, eliminating the need for diagnostic engineering support.
  • Prognostics: Offers health estimates for turbines, enabling smarter maintenance planning to maximize fleet availability and revenue.
Benefits
  • Cost Reduction: Early detection and automated diagnosis significantly lower repair and operational costs.
  • Increased Availability: Prognostic capabilities allow for optimized scheduling of maintenance, enhancing turbine availability.
  • Profitability: By optimizing operations and maintenance, the system increases the profitability of wind turbines.
Case Study
A comparison scenario illustrates the cost savings with TurbinePhD. Early detection of a gearbox bearing crack results in a $210,000 saving in repair costs and reduced downtime from 14 to 4 days, compared to traditional methods.
Conclusion
The TurbinePhD system provides comprehensive mechanical diagnostics, leveraging rotorcraft industry techniques for early fault detection, and offers tailored prognostic estimates to optimize turbine operations.
See more

Catalog excerpts

TurbinePhD? Condition Monitoring System-1

A New Standard for Condition Monitoring Increase wind turbine profitability by optimizing operations and maintenance costs and maximizing turbine availability. Early fault detection reduces maintenance costs Using industry-leading processing techniques the TurbinePhD system can detect faults before they cause secondary damage, avoiding costly down-tower repairs. Automated diagnosis reduces operating costs Automated fault diagnosis eliminates false alarms, preventing unnecessary maintenance trips, while also eliminating the need for diagnostic engineering support. Prognostics optimizes fleet availability to maximize revenue Prognostic estimates of your turbine’s health allow you to plan maintenance outages smarter, increasing fleet availability and maximizing revenue. Data Loggers Turbine Control Sensors Condition Monitoring Systems Renewable NRG Systems | Hinesburg, Vermont 05461 | USA 802.482.2255 | www.renewablenrgsystems.com

 Open the catalog to page 1
TurbinePhD? Condition Monitoring System-2

Without TurbinePhD Scenario With TurbinePhD A bearing in the high speed section of the gearbox develops a crack. Failure of bearing with Fault detected early, bearing secondary damage. Crane replaced up-tower, no crane needed, full gearbox needed. 4 days downtime. refurbishment. 14 days of downtime. Repair Costs Crane cost: Gearbox rebuild: Total: 14 days downtime Revenue Lost Total Cost Crane cost: Up-tower repair: Total: 4 days downtime TurbinePhD provides complete mechanical diagnostic coverage of the highest-value components in your turbine. Processing techniques leveraged from the rotorcraft...

 Open the catalog to page 2
*Prices are pre-tax. They exclude delivery charges and customs duties and do not include additional charges for installation or activation options. Prices are indicative only and may vary by country, with changes to the cost of raw materials and exchange rates.