GitHub / mo26-web / Induction-Motor-Faults-Detection-with-Stacking-Ensemble-Method-and-Deep-Learning

This is a induction motor faults detection project implemented with Tensorflow. We use Stacking Ensembles method (with Random Forest, Support Vector Machine, Deep Neural Network and Logistic Regression) and Machinery Fault Dataset dataset available on kaggle.

JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mo26-web%2FInduction-Motor-Faults-Detection-with-Stacking-Ensemble-Method-and-Deep-Learning

Topics: anomaly-detection, deep-neural-networks, ensemble-classifier, ensemble-learning, fast-fourier-transform, fault-detection, fault-diagnosis, induction-motor, induction-motor-fault-detection, logestic-regression, machinery-condition-monitoring, machinery-fault-dataset, random-forest, stacking-classifier, stacking-ensemble, support-vector-machine

• Files
• Readme
• Dependencies