Hi George,

I'll partition this problem into 2 pieces: (1) importing the data file; (2) taking PSD and plotting

Let's say your file looks like this:

 time1, accel1
 time2, accel2,
...
 timeN, accelN

where from your mention of CSV, I assume the pair elements are comma-separated as shown, and each pair on a separate line.

So importing:

  SetDirectory["C:\\Projects\\Accelerometer"]
  myseries = N[Import["test.txt", "Table", "FieldSeparators" -> {","}]];

The enclosing N[] is to convert any integer numbers to machine precision (for compute efficiency later)mailto:wolfram-community@wolfram.com?subject=Wolfram%20Community%20Feedback The terminating ; is to prevent the imported data from spewing to the screen

Dimensions[myseries] should yield {n,2} and myseries is organized as nested Lists as so:

{{time1, accel1}, {time2, accel2},...{timeN, accelN}}

From here it's easy:
I'll define 3 psdFunc[] functions, the latter two providing you a PSD frequency-smoothing option. Evaluate all 3.

Consider a rate signal in units of quantity/sec, and (averaged-sample) duration t sec. Output later needs to be scaled by t to get PSD in units of rate^2/Hz.

psdFunc[seq_] := (Abs[Fourier[seq, FourierParameters -> {1, -1}]]^2)/Length[seq]

We can average in the frequency domain by taking the PSDs of non-overlapping blocks of samples, then averaging the PSDs:

psdFunc[seq_, nBlocks_] := (If[Mod[Length[seq], nBlocks ] != 0, 
    Print["Non-integer multiple nBlocks!"]; Return[]]; 
    Apply[Plus, Table[psdFunc[seq[[Range[1 + (n - 1)*Length[seq]/nBlocks, (n)*Length[seq]/nBlocks]]]], {n, 1, nBlocks}]]/nBlocks);  

Or better yet (to minimize sidelobe leakage), smooth the PSD in frequency domain by applying window therein (and subsampling).

psdFunc[seq_,  nBlocks_] := (If[Mod[Length[seq], nBlocks ] != 0, 
     Print["Non-integer multiple nBlocks!"]; Return[]]; 
     Partition[psdFunc[seq], nBlocks]).Table[1./nBlocks, {nBlocks}];

Now set two parameters in the expression below -- I've put in sample values, but you should re-check/correct for your data:

1. tsamp, which is the time interval (in seconds) between samples
2. fbin, the number of raw frequency bins (of frequency resolution 1/bigT) to average

You may also want to truncate the series length to a convenient multiple of 2 as the length of myseries:

  myseries=myseries[[1;;512>, All]];

tsamp = myseries[[2,1]]-myseries[[1,1]]; 
nrec = Length[myseries]; 
bigT = nrec*tsamp;
fbin = 2;
ListPlot[tsamp*psdFunc[myseries[[All, 2]], fbin][[1 ;; nrec/2/fbin]], 
 Joined -> True, PlotRange -> All, DataRange -> {0, 1/tsamp/2}, 
 GridLines -> Automatic, Frame -> True, 
 FrameLabel -> {"Frequency (Hz)", "G^2/Hz", "Accelerometer PSD"}, 
 PlotStyle -> {{Blue, Thickness[0.005`]}, {Brown, Thickness[0.005`]}}, 
 BaseStyle -> {FontSize -> 16, FontFamily -> "Arial"}]