AeroTwin: 18,000 Synthetic Telemetry Samples for Aircraft Landing Gear

18,000 synthetic kinematic telemetry samples for predictive maintenance in MRO and Digital Twin AI applications. The dataset is hosted on Kaggle and is associated with Boeing 787 landing gear physics. The time range and original author are unknown.

Use Cases

• Training predictive maintenance models based on synthetic kinematic telemetry.
• Developing digital twin simulations for aircraft landing gear systems.
• Analyzing time-series patterns for maintenance, repair, and overhaul (MRO) planning.

Strengths

• Contains 18,000 data samples, providing a substantial base for model training.
• Focuses on a specific, high-value aviation component (Boeing 787 landing gear).

Limitations

• Data is synthetic, which may not fully capture real-world operational variance.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is known, but the specific features and data structure are unknown.

Provenance

Source

Kaggle

Collection Method

Synthetically generated telemetry data.