Using the Classification Learner App for The First Time - Current Signals Directly

-

 Since we had visualized the data and had also seen the class separability using PCA and ICA, it was time to move on to training the models. We directly fed in the three phase currents into the classification learner app.

Three Phase Currents

The group used the classification learner app to train the model, however very bad results were seen. 

The percentage accuracies were averaging at around 20 to 50%. This isn't what we required!!

We did get an accuracy of 100%! But this was due to applying the ICA data. Obviously, ICA data will give us an accuracy of 100% because it already has the classes separated. 

This is wrong!!

Now we need to move on to calculate something known as "The 15 statistical time features."

Comments

Post a Comment

Popular posts from this blog

What are Broken Rotor bar Faults?!

-
Image
Induction machines are one of the crucial elements of many industrial processes nowadays (IM). These machines are prone to rotor, stator, or bearing failure due to diverse operating circumstances. All of these IM flaws have the potential to impair output, harm neighboring equipment, or, in the worst case, lead to the system’s complete failure[1]–[3]. Induction machines have been in use since the early 1800s. The operators or the equipment close to the working zones may be at danger from this motor’s strong starting torque and high operating speeds. Broken rotor bar defects are one of the frequent causes of motor failure, which may also be brought on by an unfavorable operating environment or artificial mis-operation during the manufacturing process. The rotor bar will first fracture locally, and the surrounding area will experience increased stress. As the defect worsens, the rotor bar entirely breaks, along with any adjacent bars, and finally the whole motor collapses. The early detec
Read more

The Final Results!!!

-
Image
After training, validating and testing the models available in the Classification Learner app, the LSTM model, and the NPR tool model, it was seen that the Cubic Support Vector Machine provided the best test accuracy of 99.8% for 100% load.   The table below shows the accuracies obtained for each of the models for 12.5% of the load. Model Test Accuracy Train Accuracy ROC (AUC) LSTM     93.04%        92.69%        -    SDNN     96.9%        96.6%        -    Quadratic SVM     96.7%        98.5%        Class 1 – 0.9995        Class 2 – 0.9999        Class 3 – 0.9993        Class 4 – 0.9994        Class 5 – 0.9994    Cubic SVM     96.6%        98.7%        Class 1 – 0.9993        Class 2 – 0.9998        Class 3 – 0.9986        Class 4 – 0.9992        Class 5 – 0.9995   
Read more