Another awesome Brian Douglas video and great demonstration! I really liked how we may not actually need failure data to train models for RUL estimation in predictive maintenance and how anamoly detection can do a pretty good job. Thinking of so many applications that can use this
super cool. Does it work for systems with high nonlinearity? Larger amount of data might be needed to capture nonlinear systems and there should be considerations to make sure the detector don't freak out under its acutal dynamics and acceptable amount of disturbance. Really cool topic!
Have more standard estimators/observers from control theory, like a Luenberger observer, also been used for this? Because I can imagine that |y-yhat| (or maybe a low pass filter applied to that signal) might also be a good indicator of faults.
That’s a good point and I don’t know the answer. It seems like if the observer does a good job representing the nominal system then it could be used to flag anomalies.
Thank you so much sir for the great videos and the accurate informations you're providing. I'm a big fan! I have a suggestion for next videos: Can you talk about ML applications and approach in control theory? What are the limitations of control that favores an ML model? Thank u so much...
Another awesome Brian Douglas video and great demonstration! I really liked how we may not actually need failure data to train models for RUL estimation in predictive maintenance and how anamoly detection can do a pretty good job. Thinking of so many applications that can use this
Wow, one of the best introduction into anomaly detections, very impressive video constructed with real content!
Awesome explanation and with the example to illustrate the concepts 👏👏
I really love the example it was really coot to see an example like this one.
Excellent work
who simply explained, and very well demonstared
Great video. Lots of thanks
Really cool!
That was a great explanation.
super cool. Does it work for systems with high nonlinearity? Larger amount of data might be needed to capture nonlinear systems and there should be considerations to make sure the detector don't freak out under its acutal dynamics and acceptable amount of disturbance. Really cool topic!
Have more standard estimators/observers from control theory, like a Luenberger observer, also been used for this? Because I can imagine that |y-yhat| (or maybe a low pass filter applied to that signal) might also be a good indicator of faults.
That’s a good point and I don’t know the answer. It seems like if the observer does a good job representing the nominal system then it could be used to flag anomalies.
Excellent!
Bravo!
Thank you so much sir for the great videos and the accurate informations you're providing. I'm a big fan! I have a suggestion for next videos: Can you talk about ML applications and approach in control theory? What are the limitations of control that favores an ML model? Thank u so much...
Wonderful
Genius
Wonderful
Glad you like it!