Predictive Maintenance of Helicopter using Machine Learning

What Will You Learn?

• We will use the dataset titled “Helicopter Turboshaft Fault Detection”, which contains over 7,000
• hourly sensor readings from a helicopter turboshaft engine. The dataset includes key features such
• as:
•  Temperature of compressor and turbine
•  Compressor pressure and vibration
•  Turbine speed, fuel flow, altitude, airspeed, and ambient temperature
•  A timestamp for each reading and a fault label indicating whether the engine was in a
• normal or faulty state
• This dataset is ideal for fault classification and performance monitoring in predictive maintenance
• systems.
• Helicopter Turboshaft Detection Dataset – Kaggle
• How Machine Learning Detects Engine Failures
• 1. Collect Data – Use historical sensor data such as temperature, vibration, and pressure
• readings.
• 2. Preprocess Data – Clean and scale sensor readings, convert timestamps if needed.
• 3. Label Data – Use the Fault_Label column to train on both normal and faulty conditions.
• 4. Select Model – Use classification algorithms (e.g., Random Forest, SVM) or anomaly
• detection.
• 5. Train the Model – Learn patterns and conditions that lead to engine faults.
• 6. Evaluate the Model – Test accuracy using unseen data samples.
• 7. Predict in Real-Time – Apply the model to monitor live data for early fault warnings.