Detecting abnormal 'cat' behaviour via Supervised Learning
At what resolution are you performing you classification? At sample level?

If so, what does your baseline model look like? Did you try for example just feeding the last 5-10 raw samples as features and see what accuracy you get that way?

Also, you could try seeing what happens if you do some kind of "smoothing" at the output - e.g. if you assume that one "good" sample, surrounded by 10 "bad" samples should be reclassified as "bad" also -- because the system cannot jump so quickly between normal and faulty modes of operation.

Finally (or should be firstly), what is the accuracy that is actually necessary for your model to be useful in practice?