This is a public Edge Impulse project, use the navigation bar to see all data and models in this project; or clone to retrain or deploy to any edge device.
Simple Akida Test
Detect ball bearing faults using a subset of the Dataset for Sensorless Drive Diagnosis.
About this project
This project uses a subset of the Dataset for Sensorless Drive Diagnosis to train a compact classifier that identifies a ball bearing fault in a two-phase AC motor.
The raw data from the dataset is input current into the motor. This data is downsampled to 10kHz before training.
For more detail on the dataset, see the UCI Machine Learning Repository page or the paper below:
PASCHKE, Fabian ; BAYER, Christian ; BATOR, Martyna ; MÖNKS, Uwe ; DICKS, Alexander ; ENGE-ROSENBLATT, Olaf ; LOHWEG, Volker: Sensorlose Zustandsüberwachung an Synchronmotoren, Bd. 46. In: HOFFMANN, Frank; HÜLLERMEIER, Eyke (Hrsg.): Proceedings 23. Workshop Computational Intelligence. Karlsruhe : KIT Scientific Publishing, 2013 (Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik am Karlsruher Institut für Technologie, 46), S. 211-225
Run this model
Dataset summary
Data collected
2m 39sSensor
audio @ 10KHzLabels
ball-bearing-fault, normalProject info
| Project ID | 114197 |
| Project version | 1 |
| License | No license attached |
| No. of views | 10,079 |
| No. of clones | 7 |
