Detecting Pattern from Signal Data by Gaussian Mixture Model? I'm a machine learning newbie.

I have sensor data which is generated by several sensors.

The data is a series of 'time's. (it is not labeled, in other words, I cannot know which sensor generates which 'time').

And each sensor basically generates data periodically, but sometimes it generates other data. 

For example, there are two sensors (sensor A generates a 'time' every 2 seconds, sensor B generates a 'time' every 5 seconds.).

Then the data can be: {2.1, 4.3, 5.2, 6.1, 7.9, 9 (noise from one of the sensors), 10.1, 10.2} .... (there are some noises)

What I want to know is that, when another data is given, it is possible to know that the same sensors generate it? (We know the period)

What method should I have to use?

I found the Gaussian Mixture Model. Am I right to use it?

Thanks.
If the data is cyclic by its nature the best thing would work using its spectrum.

You can easily build a system which checks sub set of data to verify periodic and the once you establish your groups checking the affinity of new data is easy - add it to each series. It should belong to the one creates less spread in the frequency (Namely, it follows the cycles).