The Solution: XMPro iBOS for Predictive Maintenance for Wind Turbines

XMPro’s solution leverages IoT sensors, advanced data analytics, and machine learning to predict and prevent turbine failures. By employing a combination of granular rule logic and AI, XMPro provides detailed insights into the remaining useful life of components and delivers actionable predictive maintenance recommendations. This proactive approach helps optimize turbine performance, reduce downtime, and extend asset lifespan, ensuring efficient and reliable wind energy production.

Figure 1. Wind Turbine Condition Monitoring Data Stream
This renewable condition monitoring data stream for a wind turbine begins by reading all records from various data sources, including an OPC UA server. The data is then joined and contextualized with sensor data to provide a comprehensive view. Calculated metrics, such as oil levels, are derived from the sensor data. The processed data is broadcasted to multiple endpoints for further analysis. A failure recommendation rule is applied to identify potential issues, and the data is rounded and filtered specifically for wind turbines. 

Figure 2. Wind Turbine Predictive Maintenance Data Stream 
This wind turbine predictive maintenance data stream collects and processes various data inputs to predict turbine failures and optimize maintenance. The flow begins with collecting wind speed and direction, temperature, vibration, and sensor health data from different sources such as historians, OPC devices, and Azure IoT Hub. These inputs are normalized and joined with contextual data from SAP and Azure Digital Twin, which provides turbine make, model, location, and sensor context. The combined data undergoes transformations to convert types for calculations and merges with additional operational and health data. The data is then used to calculate turbine performance metrics and broadcast results to different predictive models. A binary classification model filters turbines likely to fail, followed by a regression model to estimate the remaining useful life (RUL). Anomalies and RUL are further analyzed, and recommendations are generated. The data is updated to Azure Digital Twin and ADX for continuous monitoring and maintenance actions, ensuring proactive and efficient wind turbine management.

Figure 1. Erosion Prediction Recommendation
This recommendation for wind turbine MMWT003 identifies an erosion prediction greater than 4. The event data provided includes gearbox oil level (low), rotor specific temperature (11), reading number (2414652), timestamp (Dec 8, 2023, 11:13 AM), rotor temperature (34.73578261199211), and wind speed (14.43500123658916). Users can add notes, assign the recommendation, share it, and create a work request with special instructions for maintenance. The system tracks whether the recommendation solved the problem and provides options to mark it as a false positive or resolved. This setup ensures timely response to potential erosion issues, optimizing turbine performance and longevity through proactive maintenance actions.

Figure 2. Configure With Granular Rule Logic and AI
This erosion prediction recommendation configuration combines granular rule logic and AI to provide remaining useful life and other predictive maintenance recommendations for wind turbines. The interface enables selecting metrics such as gearbox oil level, rotor temperature, and wind speed, and setting specific thresholds to trigger alerts. AI algorithms enhance the predictions by analyzing patterns and trends in the data. Users can categorize recommendations, enable execution order, and auto-escalate critical issues, ensuring comprehensive and proactive turbine maintenance.

Figure 3. Close The Loop On Event Response
Closing the loop on event response, the system can take various actions, including sending email and SMS notifications for new recommendations, status changes, note updates, and pending times. Additionally, it can automatically create work orders, send information to ERPs, and execute other predefined actions, ensuring comprehensive monitoring and immediate response to turbine issues with detailed guidance and timely alerts.

Figure 1. Real-Time Renewable Asset Overview Dashboard for Wind and Solar Farms

This advanced dashboard provides operators of renewable energy assets, specifically wind and solar farms, with a comprehensive view of their infrastructure. It features an interactive map that dynamically updates with the condition of various renewable assets, including wind turbines and solar panels, offering a clear visual representation of the operational health of these energy systems. Each asset on the map is marked with a color-coded status icon, indicating its current operational state, including active status and any alerts or error messages related to performance or maintenance needs. 

Figure 2. Asset Class Drill Down View – Wind Turbine Health in Renewable Energy Farms

This dedicated asset view for wind turbines in renewable energy farms offers a detailed and comprehensive dashboard, providing key insights into the condition and health of each turbine. 

Figure 3. Asset Analysis View – Wind Turbine Health

This Asset Analysis View offers detailed insights into specific wind turbines within a renewable energy system, focusing on a turbine identified as MMWT003.