Labeled Time Series Data of Force/Torque for Monitoring Assembly Processes with a Delta Robot

This dataset comprises 524 recordings of 6-dimensional time series data, capturing forces in three directions and torques in three directions during the assembly of small car model wheels. The data was collected using an equidistant sampling method with a sampling period of 0.004 seconds. Each time series represents the process of assembling one wheel, specifically the placement of a tire onto a rim, and includes a label indicating whether the assembly was successful (OK). The wheels were assembled in batches of four, and the recordings were obtained over six different days. The labels of recordings from two (days 3 and 4) of the six days are invalid as described in [1].  The labels presented in this data set are only binary (they do not describe the reason of the failure). The labels of recordings from days 5 and 6 are created by human while the other labels came from a convolutional neural network based computer vision classifier and can be inaccurate as described in section 5.4 of [1].   

Dataset Structure:

• File: ForceTorqueTimeSeries.csv
  • Columns:
    • idx (1-524): Index of the recording corresponding to the assembly of one wheel.
    • label (true/false): Indicates whether the assembly was successful (TRUE = product is OK).
    • meas_id (1-6): Identifier for the day on which the recording was made (refer to Table 2.1 in [1]).
    • force_x: X-component of the force measured by the sensor mounted on the delta robot's end effector.
    • force_y: Y-component of the force.
    • force_z: Z-component of the force.
    • torque_x: X-component of the torque.
    • torque_y: Y-component of the torque.
    • torque_z: Z-component of the torque.

Additional Files:

• IMG_3351.MOV: A video demonstrating the assembly process for one batch of four wheels.
• F3-BP-2024-Trna-Ales-Ales Trna - 2024 - Anomaly detection in robotic assembly process using force and torque sensors.pdf: Bachelor thesis [1] detailing the dataset and preliminary experiments on fault detection.
• F3-BP-2024-Hanzlik-Vojtech-Anomaly_Detection_Bachelors_Thesis.pdf: Bachelor thesis [2] describing the data acquisition process.

References:

1. Trna, A. (2024). Anomaly detection in robotic assembly process using force and torque sensors [Bachelor’s thesis, Czech Technical University in Prague].
2. Hanzlik, V. (2024). Edge AI integration for anomaly detection in assembly using Delta robot [Bachelor’s thesis, Czech Technical University in Prague].