David Schwarz / Ball Bearing Fault Detection

Detect ball bearing faults using a subset of the Dataset for Sensorless Drive Diagnosis.

Audio

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 ; MÃ–NKS, Uwe ; DICKS, Alexander ; ENGE-ROSENBLATT, Olaf ; LOHWEG, Volker: Sensorlose ZustandsÃ¼berwachung an Synchronmotoren, Bd. 46. In: HOFFMANN, Frank; HÃœLLERMEIER, Eyke (Hrsg.): Proceedings 23. Workshop Computational Intelligence. Karlsruhe : KIT Scientific Publishing, 2013 (Schriftenreihe des Instituts fÃ¼r Angewandte Informatik - Automatisierungstechnik am Karlsruher Institut fÃ¼r Technologie, 46), S. 211-225

Download block output

Title	Type	Size
Raw data training data	NPY file	1424 windows
Raw data training labels	NPY file	1424 windows
Raw data testing data	NPY file	148 windows
Raw data testing labels	NPY file	148 windows
NN Classifier model	TensorFlow Lite (float32)	260 KB
NN Classifier model	TensorFlow Lite (int8 quantized)	67 KB
NN Classifier model	TensorFlow SavedModel	246 KB
NN Classifier model	Keras h5 model	241 KB

Clone project

You are viewing a public Edge Impulse project. Clone this project to add data or make changes.

Summary

Data collected

2m 39s

Project info

Project ID	48672
Project version	4
License	No license attached