Published February 23, 2025 | Version V1.0

Lenze Motor Bearing Fault Dataset (Lenze-MB)

Description

The vast majority of approaches developed in the field of condition monitoring rely on public data recorded in laboratory conditions with high-performance measurement equipment. Moreover, primarily vibration data is used for condition monitoring tasks, which is particularly sensitive to typical error patterns in rotating machinery. These conditions are difficult to maintain in industrial environments since using high-performance measurement systems would not be economically feasible. This experiment investigates the simulation of realistic bearing damage with data from sensors that already exist. The test bench allows for the examination of bearings outside the engine with defined faults. Fault detection is performed exclusively using the existing drive, consisting of a Lenze i950 inverter and a permanently excited synchronous machine with a SinCos-encoder. The following variations within the setup can be investigated: bearings with defined faults; coupling with a load engine via a defined tensioned belt and defined tension force to apply radial forces; mechanical load torque.

For a detailed description of the experiment, refer to the technical report included in the files section, please.

For details on the dataset structure, please refer to the Quick_Start_Script, also found in the files.

Also you might have a look in some scientific work building on top of Lenze-QI found in the reference section below.

Files

Data.zip

Files (3.2 GB)

Name Size
md5:e9e37447032c2d433bba54ada35ac7ce
3.2 GB Preview Download
md5:3fddcea989d973ca552031094702794a
663.5 kB Preview Download
md5:5394bf733f35ad1405f58b832433be05
20.6 kB Download
md5:045dc28467b60f032485942471b595d4
256.8 kB Preview Download

Additional details

References

  • Mueller, Philipp N., Lukas Woelfl, and Suat Can. "Bridging the gap between AI and the industry—A study on bearing fault detection in PMSM-driven systems using CNN and inverter measurement." Engineering Applications of Artificial Intelligence 126 (2023): 106834.
  • Mueller, Philipp N. "Attention-enhanced conditional-diffusion-based data synthesis for data augmentation in machine fault diagnosis." Engineering Applications of Artificial Intelligence 131 (2024): 107696.