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C3 IoT > Case Studies > Predicting, Diagnosing and Reducing Equipment Failures
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Predicting, Diagnosing and Reducing Equipment Failures

Predicting, Diagnosing and Reducing Equipment Failures - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Predictive Analytics
Applicable Industries
  • Utilities
Applicable Functions
  • Maintenance
Use Cases
  • Predictive Maintenance
The Challenge

One of Europe’s largest integrated electric power companies was looking for analytics solutions to reliably forecast equipment failure and improve condition-based maintenance for its coal-fired power plant. With a diverse array of coal, oil, and gas/CCGT power plants, the utility’s more than 50GW worldwide generating portfolio has been under pressure to streamline global operations and reduce generating costs (both CapEx and operations /maintenance O&M expenses) by 7-10%.

About The Customer
One of Europe’s largest integrated electric power companies
The Solution

The company completed a successful deployment of C3 Predictive Maintenance™, demonstrating the capability of software analytics to accurately forecast equipment failure and improve condition-based maintenance at a 2,640 megawatt conventional coal-fired power plant. With a diverse array of coal, oil, and gas/CCGT power plants, the utility’s more than 50GW worldwide generating portfolio has been under pressure to streamline global operations and reduce generating costs (both CapEx and operations /maintenance O&M expenses) by 7-10%. The C3 Predictive Maintenance application was deployed to significantly contribute to these goals through multiple levers: • Improving prognostic lead time and flexibility in scheduling of maintenance tasks • Increasing temporal accuracy and localization of asset failure predictions • Reducing or avoiding unplanned, emergency maintenance tasks • Maximizing energy production reliability and dispatch commitments One of C3 IoT’s SaaS applications, C3 Predictive Maintenance employed advanced machine learning-based algorithms to enhance failure prediction and diagnostic capabilities of plant operators. The application augmented traditional systems by continuously monitoring all instrument signals, tracking complex failure modes, and detecting operating anomalies associated with impending equipment failures for a large range of rotating equipment components. With the ability to integrate broad sensor data as well as unstructured maintenance, work orders, and operations information, the application gave plant operators a comprehensive and predictive view of the current conditions and emerging maintenance requirements of equipment days and weeks ahead. Based on this successful deployment, C3 IoT is working with the utility to scale the machine learning-based methods of C3 Predictive Maintenance to a larger set of rotating equipment and systems across the utility’s conventional power plants in Europe, and additional plants worldwide in 2016-2017.

Data Collected
Fault Detection, Maintenance Requirements, Operating Cost, Overall Equipment Effectiveness, Per-Unit Maintenance Costs
Operational Impact
  • [Efficiency Improvement - Maintenance]
    Improving prognostic lead time and flexibility in scheduling of maintenance tasks
  • [Efficiency Improvement - Maintenance]
    Increasing temporal accuracy and localization of asset failure predictions
  • [Efficiency Improvement - Productivity]
    Reducing or avoiding unplanned, emergency maintenance tasks. Maximizing energy production reliability and dispatch commitments.

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