Main Bearing Fault Prediction and Management with Delfos
2 min

Main Bearing Fault Prediction and Management with Delfos

How Delfos identified and monitored a fault in the main bearing of a wind turbine, preventing emergencies and maximizing energy production

Context

Predicting failures in critical assets is essential to ensure maximum efficiency and reduce operating costs in wind turbines. The Delfos Prediction module is designed to continuously monitor the operational behavior of essential components and provide alerts based on high-precision predictive models.

In the case analyzed, Delfos detected an anomaly in the operational behavior of the main bearing of a wind turbine on day D+0. This anomaly, identified by the predictive model, indicated a deviation in the component's temperature pattern from the learned history. The fault was monitored for 295 days until a borescope inspection confirmed the damage to the component.

Figure 1: Main Bearing Temperature Deviation (ºC) x Days |  Failure Prediction in the main bearing: from the onset of failure detection to the end of the bearing's service life.

Implemented solution

After confirming the damage, Delfos adjusted the focus of predictive monitoring to follow the final stage of the bearing's useful life, allowing palliative maintenance strategies to be applied. This process included partial grease changes, which stabilized the temperature of the component, ensuring that it continued to operate until the end of the wind season, maximizing energy production before scheduled replacement.

Results

During the 335 days of monitoring, the turbine remained in operation, accumulating 7707 hours of operation and generating 4876.14 MWh of energy, with only 48 hours of limited time for maintenance adjustments.
Bearing maintenance was carried out after the wind season, eliminating emergency costs and minimizing operational impacts.

Figure 2: Effect of palliative maintenance on the temperature of the bearing. | The red cloud of markers represents the situation before the palliative actions. The blue pattern represents the situation after the palliative actions.

Conclusion

The case demonstrates the importance of Delfos' predictive monitoring in critical failure management. The ability to predict and manage the service life of the main bearing enabled the customer to avoid unscheduled downtime, reduce costs and maximize the efficiency of the turbine.

Benefits

  • Failure forecast 295 days in advance;
  • Reduced costs by avoiding emergency maintenance;
  • Maximization of production with 4876.14 MWh generated during monitoring;
  • Only 48 hours of operational limitation over the period.

Delfos proves to be essential in optimizing the operation of critical assets, ensuring greater reliability and return for its clients in the renewable energy sector.

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