Hi Radu,
I normally use specialized software for vibration analysis. I use NI data acquisition for ad hoc prototyping and use matlab for back end data analysis during prototyping. I've used Labview in the past but found it fairly cumbersome compared to a programmatic language.
First of all, the difference between Acc, Vel and Disp: for your purposes there is absolutely no need to integrate all the way to D. What will happen if you do is that frequencies say above 100Hz or so are suppressed to the extend that FFT analysis yields nothing useful. Particularly if you sample at 10kHz, most of your spectrum isn't going to show anything useful. For this application we always use 100mV/G accel sensors and in most cases I simply diagnose in Accel though many of our customers add Vel to it as well (Vel measurements are such a standard thing to do in the industry, there is no way escaping them). What you should keep in mind is that between Acc and Vel the unity gain is at 61.4Hz so an accel signal at 6.14Hz is 10 times higher in Vel. Consequently a signal at 614Hz is 10 times smaller in Vel.
We never diagnose during grinding, always during idle. This because we're interested in the condition of the bearing and misalignment or unbalance. One cannot perform this measurement while grinding as the grinding process adds a tremendous amount of vibration. Since you mentioned to be interested in measuring during grinding I cannot tell you much here. Measurements taking during idle can be as much as 0.25Gp as I have seen but in general you need to relate the actual overall level to the size and type of the spindle. Bearing failures have a very characteristic behavior and actual signal amplitude of the bearing defect signals is actually not the first parameter I look at before calling it a failure. It is the harmonic pattern I am interested in.
As far as a spectrum calculation, I use a simple FFT (not a power spectrum) as this is standard in the industry and provides the best visibility on higher frequencies (power spectra lower a frequency's amplitude by the root of the actual frequency).
Sooh, what to look for in your spectra? First determine which failures you want to capture, than determine what that failure looks like in time domain (e.g. unbalance looks like a sine, a bearing defect looks like an impact/pulse) than determine what that time domain signal looks like in a spectrum.
In general I would recommend a customer to baseline a series of known good spindles and use that baseline to set simple overall value alarm levels. In addition, I would make sure that from all spindles the bearing frequencies are known (determined by the bearing type) and watch for frequencies to pop up that correspond to the bearing frequencies. These are best monitored by a straight simple accel FFT as well as a enveloped accel FFT. In order to help automate all this data diagnosing we have specialized routines that sort of crunch the spectra down to a single number that indicates the likelihood of a bearing defect being present.
There are several bearing databases that could tell you which defect frequencies they produce. Some of the databases are free or cost $300-500 bucks or so. If you know your bearings I can see if I can find the frequencies in the databases I have here.
Hope this helps.
Joost
