No, we need something better than that.  I have developed our own modules that can detect the peaks and noisefloor features based on Principal Component Analysis.  Actually that is the code that is being used to label the dataset.  We need a DNN that can:

Identify Harmonics, in the signal wave.
Identify Non-Harmonics in the signal wave.
Find Bessel functions in the signal wave.
Identify carrier frequencies in the signal wave.
Compare two signals to find differences.

The biggest issue is that we can ID parts using simple signal processing in the laboratory, but this has to go out in production and ultimately customer environments, where there will be no hand-holding.  A neural network is the best solution because of its’ ultimate reliability.  So we need a DNN that is trained to find these features.
I wanted to base this off of audio processing (since so much work has already been done).  But it seems that ML for this kind of work only looks at a few solutions:

XY data (none of the solutions I have seen look at signal data this way).
Images of plots.  (This appears more promising, but it is hard to see how we could train a network to “Look” at plots sucessifully).
Spectrographs.  All the audio processing examples I have seen look at audio files in this manner.  I looked over Magneta and a few examples online.  I don’t think we can turn our data into spectrograms.  We have power v frequency plots (the data has already undergone an FFT).

Is there a DNN effort that has looked at ways to process Power Vrs Frequency plots in radio emissions?
Poking around the web I found this company in VA: https://www.deepsig.ai/ 1.  Looks like we have competition.