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
@trollgarten11 ай бұрын
Wow, one of the best introduction into anomaly detections, very impressive video constructed with real content!
@mario1ua19 күн бұрын
Really cool!
@srodrmmabet6381 Жыл бұрын
Awesome explanation and with the example to illustrate the concepts 👏👏
@Via.Dolorosa Жыл бұрын
who simply explained, and very well demonstared
@standardio8270 Жыл бұрын
I really love the example it was really coot to see an example like this one.
@mukhtarsani987110 ай бұрын
Excellent work
@ruoxixi20 Жыл бұрын
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!
@zerddrez235 Жыл бұрын
Great video. Lots of thanks
@kwinvdv Жыл бұрын
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.
@BrianBDouglas Жыл бұрын
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.
@posthocprior Жыл бұрын
That was a great explanation.
@sarette509 Жыл бұрын
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...