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

[1] Bator, Martyna & Dicks, Alexander & Mönks, Uwe & Lohweg, Volker. (2012). Feature Extraction and Reduction Applied to Sensorless Drive Diagnosis. 10.13140/2.1.2421.5689.

[2] 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. [2] 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.