How many times do wind turbines in a wind farm shut down per year? A large part of the loss is not due to the fault itself, but because the fault was discovered too late - waiting until there were major problems with the gearbox and blades before shutting down for maintenance, which could result in several weeks of lost power generation at once. The problem that AI predictive maintenance needs to solve is precisely this: informing you in advance that the device is "about to break down" before it actually breaks down.
What is predictive maintenance
There are two types of traditional maintenance: "post repair" that repairs when it breaks down, and "preventive maintenance" that maintains according to a fixed cycle. Predictive maintenance is the third approach - using sensors to continuously monitor equipment vibration, temperature, oil and other data, combined with AI models to determine equipment health status, and arrange maintenance before faults occur. The goal is not to 'never damage', but to 'repair at the right time'.
What does AI do on energy devices
Wind turbines are the most typical scenario: sensors installed in the engine room and tower continuously collect vibration and temperature data, AI models learn the signal patterns of the equipment during normal operation, and once abnormal symptoms occur (such as gradually increasing vibration in a specific frequency band), advance warnings are issued. The operation and maintenance team can take advantage of the window period of low wind to arrange maintenance and avoid small problems from turning into major failures.
The common losses of photovoltaic power plants come from module heat spots, decreased inverter efficiency, and dust cover. By analyzing the power generation data and meteorological data reported by the inverter, AI can identify clusters with consistently low power generation, prompt on-site inspections, and reduce "invisible power generation losses".
Transformers and transmission lines on the grid side can also be connected to the monitoring system, gradually shifting inspections from "running on-site according to plan" to "data-driven, on-demand deployment".
What conditions are required for landing
The value of predictive maintenance has been widely validated in the energy industry: reducing unplanned downtime, shortening average maintenance time, and optimizing spare parts inventory. But the specific benefits are highly dependent on data quality and scenario selection. To land, typically three things are needed: reliable sensor data, sufficiently long historical fault records, and a team that understands both the equipment and the data. For small and medium-sized power plants, it is not necessary to install a complete system from the beginning. They can start by piloting the most valuable and problem prone equipment (such as wind turbine gearboxes), and gradually expand after they are operational.
Conclusion
The larger the scale of new energy installation, the greater the pressure of operation and maintenance. AI predictive maintenance will not prevent equipment from never being damaged, but it can ensure that every maintenance occurs at the right time - for the energy industry, saving one hour of downtime is a tangible benefit.
【 Reference source 】 Comprehensive compilation of industry information and engineering practice discussions that have been publicly released.