Edge Impulse Inc. / Sensorless Drive Diagnosis Feature Classifier
This is your Edge Impulse project. From here you acquire new training data, design impulses and train models.
About this project
This project uses pre-extracted features from the Dataset for Sensorless Drive Diagnosis
To evaluate the ability of a NN classifier to detect different types of AC motor faults based on 48 statistical features. Information on the feature extraction may be found in the citation below.
Note that the pre-computation of these features may be done using a custom DSP block within edge impulse. For more information and an example see: https://github.com/edgeimpulse/edge-impulse-emd-feature-dsp-block
 Bator, Martyna & Dicks, Alexander & Mönks, Uwe & Lohweg, Volker. (2012). Feature Extraction and Reduction Applied to Sensorless Drive Diagnosis. 10.13140/2.1.2421.5689.
 F. Paschke, C. Bayer, M. Bator, U. Mönks, A. Dicks, O. Enge-Rosenblatt, and V. Lohweg, “Sensorlose Zustandsüberwachung an Synchronmotoren,” in Proceedings 23. Workshop Computational Intelligence, Karlsruhe: KIT Scientific Publishing, 2013, pp. 211–225.  C. Bayer, M. Bator, U. Mönks, A. Dicks, O. Enge-Rosenblatt, and V. Lohweg, “Sensorless Drive Diagnosis Using Automated Feature Extraction, Significance Ranking and Reduction,” in 18th IEEE Int. Conf. on Emerging Technologies and Factory Automation (ETFA 2013): IEEE, 2013, pp. 1–4.
Creating your first impulse (100% complete)
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.
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
Data collected46m 47s