Edge Impulse Inc. / AC Motor Fault Detection
Detect bearing failure, axis inclination, and shaft misalignment faults for two phase AC motors. Data obtained from the Dataset for Sensorless Drive Diagnosis by Martyna Bator. Electric current drive signals are measured. The drive has intact and defective components. This results in 11 different classes with different conditions. Each condition has been measured several times by different operating conditions, this means by different speeds, load moments and load forces. The current signals are measured with a current probe and an oscilloscope on two phases. source: https://zenodo.org/record/35577#.YQFb9FNKgpV
Creating your first impulse (100% complete)
Acquire data
Every Machine Learning project starts with data. You can capture data from a development board or your phone, or import data you already collected.
Design an impulse
Teach the model to interpret previously unseen data, based on historical data. Use this to categorize new data, or to find anomalies in sensor readings.
Deploy
Package the complete impulse up, from signal processing code to trained model, and deploy it on your device. This ensures that the impulse runs with low latency and without requiring a network connection.
Download block output
Title | Type | Size | |
---|---|---|---|
MFE training data | NPY file | 7480 windows | |
MFE training labels | NPY file | 7480 windows | |
MFE testing data | NPY file | 440 windows | |
MFE testing labels | NPY file | 440 windows | |
NN Classifier model | TensorFlow Lite (float32) | 106 KB | |
NN Classifier model | TensorFlow Lite (int8 quantized) | 34 KB | |
NN Classifier model | TensorFlow Lite (int8 quantized with float32 input and output) | 34 KB | |
NN Classifier model | TensorFlow SavedModel | 114 KB |
Clone project
Summary
Data collected
14m 40sProject info
Project ID | 41083 |
Project version | 4 |
License | No license attached |