Predictive Maintenance
EnBW needed to move beyond reactive, manual analysis of massive wind-turbine telemetry so it could detect faults and anomalies early across a heterogeneous fleet before they caused expensive failures or downtime.
Reported impact
EnBW detects turbine faults and anomalies earlier, can reduce output before a complete failure occurs, cuts maintenance and failure costs, and improves the availability of renewable-energy generation.
EnBW built an in-house AI and machine-learning workflow for condition and structural health monitoring across its onshore wind fleet. Each turbine contributes operating telemetry from up to 1,500 sensors, with readings such as bearing temperature, gear-oil temperature, generator speed, wind speed, rotational speed and output arriving continuously, and core operating data sent every ten minutes. EnBW correlates these measurements with environmental and operating parameters, then feeds them into thousands of specialized models that learn normal behavior for specific turbine types, components and site conditions. When a model detects deviation from the learned baseline, the system alerts specialists, who review the abnormal pattern with engineers and technical experts, estimate severity and decide whether to dispatch service teams. If a serious failure is imminent, the team can proactively restrict turbine output to protect damaged parts, buying time for maintenance while improving availability and reducing repair cost.
Telemetry from wind-turbine sensors and anemometers flows into EnBW's monitoring environment, where operating and environmental parameters are correlated and used to train and run specialized machine-learning models. Deviations from normal behavior trigger alerts for specialists, engineers and technical experts, who validate the issue and, if needed, send service teams or reduce turbine output to protect equipment.
"By doing so, they are already saving EnBW around ten million euros per year."
"Every ten minutes, each wind turbine sends its operating data."
"If, for example, they detect that a serious fault is imminent and there is risk of a complete system failure, they restrict the wind turbine’s output."
"The EnBW team currently monitors 450 wind turbines with the aid of AI."
"The pair are working with artificial intelligence (AI) to subdue the data storm and thus enable them to detect faults and anomalies in the turbines at an early stage."
"We feed the model with these values – and it then learns the dependencies between the individual measured parameters. This is how the model simulates a certain normal behavior."
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
Related
In-house Sample Est. · Jul 10, 2026 Aramco Aramco needs to turn extremely large and complex exploration, drilling, refining, logistics, and market datasets into faster, safer, and more reliable operational decisions while reducing uncertainty, downtime, and risk… five facilities recognized by the World Economic Forum as Lighthouse sites Dammam-7 · Ghawar-1 · Operations Co-Pilot In-house Sample Jan 7, 2026 Equinor Equinor needs to maintain safe, efficient, and profitable operations across offshore platforms and land facilities while improving geological understanding, planning wells and field development better, reducing… 2 million square kilometres In-house Sample Mar 14, 2025 Chevron Chevron needs to keep energy assets operating reliably, detect issues such as leaks early, and improve subsurface decision-making so crews can maintain safe, efficient production while supporting rising power demand… a second Percepto system in Colorado Percepto system 