- Proficiency in Predictive Analytics Tools: Ability to use advanced tools like IoT sensors, data analytics, and machine learning to analyze historical data and predict potential equipment failures.
- Data Analysis and Interpretation Skills: Knowledge of how to interpret data from various sensors and analyze it to identify patterns and potential issues before they escalate.
- Expertise in IoT and Connected Systems: Understanding of how IoT sensors work and how they are integrated into a network to track equipment activity.
- AI-Driven Diagnostics Skills: Familiarity with AI-driven diagnostics to identify potential equipment malfunctions early.
- Vibration and Thermal Imaging Analysis: Ability to use vibration analysis and thermal imaging to detect issues such as gearbox wear or misalignments in wind turbines.
- Knowledge of Renewable Energy Systems: Comprehensive understanding of wind, solar, and other renewable energy systems to effectively manage and maintain them.
- Troubleshooting and Problem-Solving Abilities: Ability to troubleshoot issues identified by predictive maintenance and solve problems proactively.
- Familiarity with Harsh Weather Conditions: Understanding of how severe weather impacts renewable energy installations and the ability to analyze historical data to model future scenarios.
Original article by NenPower, If reposted, please credit the source: https://nenpower.com/blog/what-are-the-key-competencies-needed-for-predictive-maintenance-in-renewable-energy/
