Predictive maintenance is becoming increasingly common as the scale, size, and number of solar and wind power installations expand. According to a new report, this approach is helping to improve efficiency, reduce operational expenses, and mitigate unplanned outages.

Power Technology‘s parent company GlobalData’s Predictive Maintenance in Power report highlights that the uptake of predictive maintenance is growing as the focus on cost efficiency and effective operations intensifies.

The report emphasizes that the effectiveness of predictive maintenance will be significantly enhanced through the application of artificial intelligence (AI). It states: “The ability of generative AI to learn from existing data sets will generate new insights, making it a powerful tool for enhancing predictive maintenance strategies. The combination of predictive maintenance and generative AI will revolutionize how power companies approach equipment maintenance, leading to increased productivity, reduced breakdowns, and lower maintenance costs.”

Predictive maintenance in power

The nature of maintenance in the 21^st century has shifted from reactive to proactive. In the power industry, this is especially significant due to the sector’s need for efficient and reliable power generation and supply. A proactive maintenance approach can extend equipment lifespan, mitigate system outages, and improve overall efficiency, all contributing to cost savings.

Various methods are employed to monitor solar, wind, and other power technologies to anticipate potential system failures caused by machinery deterioration, such as misalignment, leakages, friction, and overheating.

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Techniques used to assess potential asset deterioration, such as turbines, include vibration monitoring, infrared thermography, lubricant oil analysis, and ultrasonic and acoustic monitoring.

Generative AI in predictive maintenance

The ability of AI and machine learning to process and analyze large datasets means that potential issues can be identified more easily and accurately than ever before. This allows for machine servicing to occur only when necessary, reducing downtime.

Moreover, the report indicates that with more accurate forecasting of when machines need replacement, businesses can maintain a leaner inventory, holding only essential parts and reducing excess stock.

Generative AI is enhancing the existing benefits of predictive maintenance in the power sector. One company utilizing this technology is German automation firm Siemens, which introduced generative AI functionality into its Senseye Predictive Maintenance in February 2024. This solution uses AI to generate machine and maintenance behavior models, directing user attention where it is most needed. According to Siemens, this leads to up to an 85% improvement in downtime forecasting and a 50% reduction in unplanned machine downtime.