How to predict degradation of the low speed main bearing and thereby reduce production losses resulting from the long delivery time of the critical part?
The P4A system has been implemented on a wind turbine located in France. In 2017 the system detected increasing vibrations around the main bearing. This trend was in line with an abnormal pressure on the lubrication circuit.
Replacing a major component of a wind turbine is challenging and costly. The main bearing is probably one of the most challenging parts of this asset as it has a 6-month lead time and can only be replaced using cranes. In case the degradation of the main bearing is not noticed in time, there is a serious risk of production losses due to operations at power limitation or complete downtime of the wind turbine.
Predictive maintenance is the best way to efficiently plan the main bearing replacement and reduce production losses. Early bearing damage is usually detected through a vibration monitoring system, called condition monitoring system (CMS). All bearing damage parameters are compared to a standardized threshold (P4A-model type I). The CMS can send an automatic alarm as soon as the bearing starts degrading.
Through vibration analysis, damage can usually be detected 3 to 6 months before the failure occurs. On a main bearing however, damage can only be detected at an advanced stage because of the low rotating speed (rpm) and large variation in load. This late detection results in uncertainty and losses as the owner cannot operate the turbine at its full power or may even have to stop it for several months while awaiting the replacement.
First of all, P4A proposes a solution to shorten the detection delay form 6 to 1 month. Through our hybrid model we compare the CMS data with the operating conditions of the bearing retrieved from the SCADA system to detect any anomalies. The P4A hybrid model can manage the large variation of bearing load and the condition of the bearing lubrication system. Moreover, our hybrid model continues with an on-line, statistical analysis of the CMS data corrected by the SCADA data.
As part of our solution, we analyse the condition of the main bearing lubrication. This solution is based on a hybrid model of the pressure of the lubrication circuit and the operating condition of the turbine. In case of abnormal vibration and condition of the lubrication the P4A system sends an automatic notification to the maintenance operator. And thereafter, we can diagnose the root cause through a cross analysis of all health indicators.
In this case, an inspection and oil analysis confirmed a clogged filter in the lubrication circuit due to excessive metal particles. This confirmed to us that the main bearing was damaged.
Secondly, we prognose the remaining lifetime of the main bearing based on the current CMS levels and the operating conditions (such as power production) and advise the adequate power limitation allowing secure operation until the bearing replacement.
Finally, though predictive maintenance of the main bearing lubrication system we recommend better preventive maintenance such as regular oil filter replacement or oil flushing.
If the data is available the P4A system can be implemented quickly. For a minimal investment we turn your data into actionable intelligence such as process optimization and predictive maintenance.
In this case we are able to predict upcoming failures and thereby reduce productions losses and increase uptime. Furthermore our solution allows optimized and safer operations and maintenance resulting in a lifetime extension of your assets. And while the investment is minimal the average gain is almost € 25.000