ElhamDataset for Fault Detection in Quadruped Robots: Healthy and Faulty Simulation Data in PyBullet

This dataset contains simulated sensor data for a quadruped robot in both healthy and faulty conditions, generated using the PyBullet physics engine. The faulty conditions were created by restricting or altering the motion of individual joints to emulate common failure scenarios, while the healthy data represent normal locomotion. The dataset is intended to support research on fault detection, anomaly identification, and reliability analysis of quadruped robots. It can be utilized for developing and benchmarking machine learning models, as well as for testing algorithms in robotics fault diagnosis and resilient locomotion control.