Fault Detection in Wind Turbines
Fault Detection in Wind Turbines
A leading bearings manufacturer partnered with Pandita to develop solution for detecting and diagnosing bearing faults in wind turbines, addressing costly maintenance, unexpected downtime, and efficiency losses.
A leading bearings manufacturer partnered with Pandita to develop solution for detecting and diagnosing bearing faults in wind turbines, addressing costly maintenance, unexpected downtime, and efficiency losses.
60%
Reduced maintenance costs
80%
Decreased downtime
15%
Increase in energy production
85%
Faults detected
60%
60%
Reduced maintenance costs
Reduced maintenance costs
80%
80%
Decreased downtime
Decreased downtime
15%
15%
Increase in energy production
Increase in energy production
85%
85%
Faults detected
Tailored Gen AI Applications
Tailored Gen AI Applications
Frequent bearing faults in wind turbines cause unplanned downtime, high maintenance costs and reduced operation efficiency.
Frequent bearing faults in wind turbines cause unplanned downtime, high maintenance costs and reduced operation efficiency.
Traditional methods are slow, labor-intensive, and error prone.
Traditional methods are slow, labor-intensive, and error prone.
“Every week, we’re dealing with unexpected failures. It’s not just the repairs — it’s the downtime that hits us the hardest. We’re constantly firefighting instead of staying ahead of the problem.”
“Every week, we’re dealing with unexpected failures. It’s not just the repairs — it’s the downtime that hits us the hardest. We’re constantly firefighting instead of staying ahead of the problem.”
“Manually analyzing vibration data is exhausting and slow. It takes hours to go through al the FFT spectrums, and even then, we can miss early signs. We need a system that can handle this volume of data faster and more accurately.”
“Manually analyzing vibration data is exhausting and slow. It takes hours to go through al the FFT spectrums, and even then, we can miss early signs. We need a system that can handle this volume of data faster and more accurately.”
“Unplanned downtime disrupts everything — from energy production targets to maintenance schedules. It’s a constant challenge to balance maintenance needs with operational demands.”
“Unplanned downtime disrupts everything — from energy production targets to maintenance schedules. It’s a constant challenge to balance maintenance needs with operational demands.”
The Approach
The Approach
In collaboration with Pandita, the company embarked on an AI-driven transformation to address these challenges.
In collaboration with Pandita, the company embarked on an AI-driven transformation to address these challenges.
The solution focused on developing three core Machine Learning models tailored for wind turbine operations.
The solution focused on developing three core Machine Learning models tailored for wind turbine operations.
Anomaly Detection
Anomaly Detection
In collaboration with Pandita, the company embarked on an AI-driven transformation to address these challenges.
In collaboration with Pandita, the company embarked on an AI-driven transformation to address these challenges.
Diagnotics
Diagnotics
Once anomalies are detected, diagnostic models classify the faults by type and severity. Machine learning algorithms analyze patterns to determine whether issues stem from wear, misalignment, lubrication problems, or other factors.
Once anomalies are detected, diagnostic models classify the faults by type and severity. Machine learning algorithms analyze patterns to determine whether issues stem from wear, misalignment, lubrication problems, or other factors.
Prognostics
Prognostics
Prognostic models predict the remaining useful life of bearings by analysing historical failure data and current operating conditions. This allows the team to schedule maintenance activities before critical failures occurred.
Prognostic models predict the remaining useful life of bearings by analysing historical failure data and current operating conditions. This allows the team to schedule maintenance activities before critical failures occurred.
Our Process
Our Process
Our process ensures early fault detection, accurate diagnostics, and predictive maintenance, enabling seamless integration with existing systems.
Our process ensures early fault detection, accurate diagnostics, and predictive maintenance, enabling seamless integration with existing systems.
The integration of predictive capabilities enhances turbine efficiency, extends the lifespan of components, and increases energy production.
The integration of predictive capabilities enhances turbine efficiency, extends the lifespan of components, and increases energy production.
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