Devices behavior in one continuous variable vs events rate
Done with K-means clustering with descriptive statistics as features:

In short, I've tried the idea described in the question, even if I was thinking it won't work. Let the experience talk...

I initially had a list of devices data. Each element of the list were 2 columns, R rows matrix, and R was different for each device. So, per device:

[
    [mesureValue, timestamp],
    ..., 
    [mesureValue, timestamp],
]


Since I'm only interested in the measureValue distribution, I've transformed the inital data to a 8 columns, N rows matrix, where N = number of devices.

The columns are, computed on the correponding device's measure value:


Arithmetic mean
Median
First quartille
Third quartille
Minimum
Maximum
Range
Standard deviation


With this matrix, I've applied K-means clustering using scikit learn (python).

I made the link between the matrix line and the physical device by using pandas Data Frames (python) who's line index are in fact the serial number of the device.

I've tried with 5 clusters, and it works.

Just in case of, if I need improvements in the future, I'm planning to add other statistics in the columns, especially for deviation vs. normality. So, for example Kurstosis and normal q-q plot p value.

Best regards.