Hydraulic System Condition Monitoring Sensor Data

UCI Machine Learning Repository hosts a dataset for condition monitoring of hydraulic systems. It contains sensor measurements from a test rig simulating cooling, lubrication, and valve conditions. The data supports research into predictive maintenance and fault diagnosis.

Use Cases

• Predict system component health status using pressure, flow, and temperature sensor readings.
• Classify specific fault conditions like cooler degradation or valve issues from sensor time-series data.
• Train anomaly detection models to identify deviations from normal hydraulic system operation.
• Analyze the correlation between vibration or motor power sensor data and impending system failures.

Strengths

• Data collected from a controlled hydraulic test rig, ensuring structured experimental conditions.
• Includes measurements for multiple system components (cooler, valve, pump, accumulator), allowing for multi-fault analysis.

Limitations

• Specific row count, column count, and time range are not provided in the input.
• Data originates from a single test setup, which may limit generalizability to other hydraulic systems.

Provenance

Source

UCI Machine Learning Repository

Collection Method

Collected from sensors on a hydraulic test rig under various operating conditions.