1D-CNN with Multiple Kernels || Deep Learning for Bearing Condition Monitoring || Part-4 скачать в хорошем качестве

1D-CNN with Multiple Kernels || Deep Learning for Bearing Condition Monitoring || Part-4

#machinelearningtutorial #python #keras #deeplearning #neuralnet #DatadrivenFaultDiagnosis #bearing #IntelligentFaultDiagnosis #conditionmonitoring This video deals with fault diagnosis of bearing, without any preprocessing. Direct raw acceleration data is used to train 1D-CNN models with multiple kernel lengths to capture various frequency features and their performance evaluated. Outputs of hidden layers are visualized using t-SNE dimensionality reduction. PART-1 -    • Data Preprocessing || Deep Learning for Be...   PART-2 -    • ANN model training || Deep Learning for Be...   PART-3 -    • 1D-CNN for Bearing Vibration || Deep Learn...   PART-4 -    • 1D-CNN with Multiple Kernels || Deep Learn...   PART-5 -    • 2D-CNN - Vibration Signal to Image || Deep...   Email - mohandash96@gmail.com Linkedin -   / b-mohan-dash-91013a155   CWRU Dataset - https://engineering.case.edu/bearingd... GitHub Link- https://github.com/mohan696matlab/CWR...