METALLICADOUR: Detection and diagnostics of multi-axis robot faults (2023)

Related person : Moncef Soualhi [1]
[1] : Franche-Comté Electronique Mécanique Thermique et Optique - Sciences et Technologies (UMR 6174) (Université de Franche-Comté)
[2] : Laboratoire d'Analyse des Signaux et des Processus Industriels
[3] : Laboratoire Génie de production
[4] : Laboratoire Ampère
Description :
Dataset of position, current, vibration, force, and torque measurements of an electromechanical drive system. The system is a multi-axes robot that contains a three-phase asynchronous motor. This latter motor drives a cutting tool for machining aluminum parts. It studies different states of health of the robot axes and machining tool. There exist 4 different health states of the machining tool and multiple drifts in the robot axes. All the experiments are conducted under different operating conditions.
Disciplines :
computer science, artificial intelligence (engineering science), engineering, electrical & electronic (engineering science), engineering, industrial (engineering science), engineering, mechanical (engineering science), engineering, multidisciplinary (engineering science), robotics (engineering science)

General metadata

Data acquisition date : from 11 Oct 2018 to 11 Dec 2021
Data acquisition methods :
  • Experimental data :
    The test bench of METALLICADOUR Technology Transfer Center is designed for two purposes: 1) monitor the health state of a machining tool mounted on the motor spindle at the end of the robot axis, 2) monitor drifts of the robot axes. In this sub-section the dataset related to the monitoring of the machining tools is presented. The robot in use is an ABB 6660 having six axes of rotation (6 arms), each axis being an alternative servo-motor. A three-phase motor carrying the machining tool is mounted at the end of the sixth axis. The cutting tool is used for machining aluminum pieces and contains three cutting edges. Regarding the manufacturing piece, it consists of a small aluminum part used in aeronautic industry. Behind the robot, an IRC5 controller is used to power and control its axes. An inverter is also used to power the machining spindle.

    In the first case of tool monitoring, this test bench is equipped with different types of sensors. Three current sensors are placed at the output of the inverter. They correspond to each phase of the tool spindle. On the flange of the machining spindle, as close as possible to the machining tool, a three-axis accelerometer (x- axis, y-axis and z-axis) is also placed. Finally, a three-axis force sensor and a three-axis torque sensor are installed between the sixth axis of the robot and the machining spindle.

    The monitoring data are collected over a period of 5 seconds with a sampling frequency equal to 25.6 kHz and are stored in .csv files with 12 columns. Columns 1 to 3 represent the three-phase current signals, columns 4 to 6 correspond to the three-axis force signals, columns 7 to 9 are for the three-axis torque signals, and columns 10 to 12 are for the three-axis accelerometer.

    Concerning tool monitoring, 16 experiments were performed to collect the data. In total, 4 tool health states were studied: a healthy state of the tool, a tool surface damage, a tool flack damage, and a tool broken tooth. In each health state, 4 operating condition were investigated by varying 3 parameters: the cutting depth, the spindle speed and the spindle feed rate.

    In the robot axes drifts monitoring, the robot is used for machining aluminum parts, consisting of a labyrinth shape aiming to move all the robot axes, which is a different shape than the previous one.

    The previously installed sensors (three-phase current, force, torque and vibration) related to the machining tool are still valid. However, new data related to the displacement of each axis of the robot are additionally collected in this study. They are the position data from the encoders placed on the rotating shaft of each axis.

    The data set related to robot tool sensors are collected over a period of 5 seconds with a sapling frequency of 25.6 kHz in .csv files with 12 columns, while the position data of each axis of the robot are collected with a sampling frequency of 41.6 Hz in .xlsx files with 9 columns. The 12 columns of the tool data are the three-phase current, the three-axis force, the three-axis torque and the three-axis vibration signals. In the .xlsx files, column 1 is the time step of the acquisition, columns 2 to 7 are the position data of the robot axes (from axis 1 to axis 6), columns 8 to 10 are the x, y, and z coordinates of the tool center position relative to the object coordinates (user coordinates), and columns 11 to 12 are the index of the machining start time and the index of the acquisition trigger, respectively. These last two parameters are equal to 0 or 1. Zero value in both cases means that the activity of the machining and acquisition has not yet started. Otherwise, one value means that the machining process has started and the acquisition has been initiated.

    In total, 14 experiments were conducted. The first group of experiment contains one experiment that represents the healthy state of the robot without any drifts (the reference case) and 6 experiments with single drifts on each axis of the robot. The second group of experiments contains one health state without drifts of the robot and 6 health states with combined drifts between the robot axes, e.g. drifts in both axis 4 and axis 6 simultaneously.

    Two .pptx files containing the test bench figure and its scheme can be found in the METALLICADOUR dataset repository by clicking on "ACCESS TO DATA" on the home page of the site.
Update periodicity : no update
Language : English (eng)
Formats : text/csv
Audience : University: master, Research, Informal Education
Publications :
  • Soualhi, M., Nguyen, K. T., Medjaher, K., Lebel, D., & Cazaban, D. (2022). Intelligent monitoring of multi-axis robots for online diagnostics of unknown arm deviations. Journal of Intelligent Manufacturing, 1-17. (doi:10.1007/s10845-022-01919-y)
  • Soualhi, M., Nguyen, K. T., & Medjaher, K. (2020). Pattern recognition method of fault diagnostics based on a new health indicator for smart manufacturing. Mechanical Systems and Signal Processing, 142, 106680. (doi:10.1016/j.ymssp.2020.106680)
  • Soualhi, M., Nguyen, K., Medjaher, K., Lebel, D., & Cazaban, D. (2020). Data-driven diagnostics of positioning deviations in multi-axis robots for smart manufacturing. IFAC-PapersOnLine, 53(2), 10330-10335. (doi:10.1016/j.ifacol.2020.12.2769)
  • Soualhi, M., Nguyen, K., Medjaher, K., Lebel, D., & Cazaban, D. (2019, May). Health indicator construction for system health assessment in smart manufacturing. In 2019 prognostics and system health management conference (PHM-Paris) (pp. 45-50). IEEE. (doi:10.1109/PHM-Paris.2019.00016)
Collection :
Project and funder :
Additional information :
The platform is installed at the METALLICADOUR recherche and transfer technology center, in Assat, France.
Record created 6 Mar 2023 by Moncef Soualhi.
Last modification : 25 Sep 2023.
Local identifier: FR-13002091000019-2023-03-06-02.


dat@UBFC is a metadata catalogue for research data produced at UBFC.

Terms of use
Université de Bourgogne, Université de Franche-Comté, UTBM, AgroSup Dijon, ENSMM, BSB, Arts des Metiers