![\"\"](\"http:\/\/blogs.napier.ac.uk\/cwst\/wp-content\/uploads\/sites\/23\/2017\/08\/long1.gif\")
<\/a><\/td>![\"\"](\"http:\/\/blogs.napier.ac.uk\/cwst\/wp-content\/uploads\/sites\/23\/2017\/08\/long2.gif\")
<\/a><\/td>![\"\"](\"http:\/\/blogs.napier.ac.uk\/cwst\/wp-content\/uploads\/sites\/23\/2017\/08\/long3.gif\")
<\/a><\/td>\n<\/tr>\n<\/a><\/td><\/a><\/td><\/a><\/td>\n<\/tr>\n<\/a><\/td><\/a><\/td><\/a><\/td><\/a><\/td><\/a><\/td>\n<\/tr>\nWe might call the longitudinal and torsional modes harmonics because the frequencies are all multiples of the lowest frequency (“fundamental”) of that mode type.\u00a0 This is not the case for flexural vibration, so we can call them “partials”.\u00a0 In the real world the higher modes for longitudinal and torsional vibration probably are not exact harmonics.<\/p>\n
<\/p>\n
UNDER CONSTRUCTION<\/h1>\nMaking measurements<\/h3>\n
The technique is covered by several standards (e.g. ASTM E1876, ISO 12680-1, EN 843-2 and EN 14146), so the first step is to see if any standard method seems appropriate to your situation.\u00a0 We aim to get a protocol for wood into BS 373 and \/ or EN 408 but, as of now we have to adapt the standards written for other materials.\u00a0 Since wood is anisotropic we have to be careful.<\/p>\n
You will need:<\/p>\n
- \n
- Wood specimen to measure, with appropriate shape and dimensions<\/li>\n
- A hammer or appropriate size for the specimen<\/li>\n
- Supports for the specimen.\u00a0 If the specimen is normal sawn timber, saw horses are fine.\u00a0 For small specimens use knife edge supports.<\/li>\n
- A microphone to record the sound, and something to hold it in place.<\/li>\n
- A reasonably quiet environment in which to make the measurement (certainly free from any other harmonic frequencies).<\/li>\n
- A balance to measure mass (although the method is still of some use with estimated density)<\/li>\n
- And, as usually necessary for wood, some method of controlling or measuring moisture content<\/li>\n<\/ul>\n
UNDER CONSTRUCTION<\/h1>\n
R functions within TimbR<\/h3>\n
The following functions are included within TimbR.\u00a0 They require the R packages\u00a0seewave <\/a>and tuneR<\/a>\u00a0to be installed and loaded.\u00a0 The necessary input is a mono audio file\u00a0saved as pulse-code modulation waveform audio file format (“.wav”)<\/a>. You can convert other formats with tuneR functions (and sound editing software such as Audacity<\/a>), but be aware that audio file compression can modify the frequency spectrum – so we recommend uncompressed wav to begin with.\u00a0 The sound file must contain the sampling frequency, so when converting files make sure this information is retained or added.\u00a0 It is most convenient to use the filename as an ID label for the results, so bear that in mind when naming the files.<\/p>\n
quickpeaks<\/h4>\n
This may be the only function that you need to use directly.\u00a0 It is a simple and quick way to obtain peak frequencies and damping (from bandwidth method) from a sound wave containing one or more hits.\u00a0 The basic input is the wave and an ID label to attach to the results in the output.\u00a0 There are optional parameters to control the finding of hits and the finding of peaks.\u00a0 This function calls on some of the functions below, so you will need findhits, expwin, specFFT and peaksQ also loaded.<\/p>\n
findhits<\/h4>\n
This function finds the sounds of the hits in an audio file.\u00a0 It is similar in function to timer in seewave, but does not output results in the same format so is not directly interchangeable.\u00a0 Unlike timer, findhits is configured specifically for impact excitation, and so it works better for locating this kind of sound.<\/p>\n
expwin<\/h4>\n
This function applies an exponential window to reduce the end (and start) of a sound recording to zero prior to FFT analysis. This affects the damping, so when using this function directly you need to subsequently correct any damping calculation.\u00a0 The function quickpeaks does this correction for you.<\/p>\n
specFFT<\/h4>\n
This function calculates a frequency spectrum using Fast Fourier Transform (FFT).\u00a0 It is similar to the spec function in seewave, but simplified in that it is already configured to give the correct kind of output for impulse excitation measurement.\u00a0 It is based on the fft function already in R.\u00a0 Faster methods are available for long duration resonance, but for most cases this should work well enough.\u00a0 As of 4\/12\/2017 the spec function of seewave has a small error for some calculations<\/a>\u00a0and specFFT avoids this.<\/p>\n
peaksQ<\/h4>\n
This function searches a frequency spectrum for peaks, and also calculates damping (via the bandwith method) for those peaks.\u00a0\u00a0It is similar in function to fpeaks in seewave, but does not output results in the same format so is not directly interchangeable.\u00a0 The function peaksQ is configured specifically for impact excitation, and is much faster to calculate than fpeaks.<\/p>\n
logdecrement<\/h4>\n
This function calculates damping using the log decrement method.\u00a0 The input wave must be of a single hit, and, if it contains more than one frequency peak, it must be filtered about the peak of interest.<\/p>\n
simhit<\/h4>\n
This function creates an artificial impact excitation recording.\u00a0 It is useful for testing calculation scripts (since the frequencies and damping are known exactly) and for checking calculations for anomalies.\u00a0 Sounds made with simhit can be played back with the listen function of seewave.<\/p>\n
example<\/h4>\n
This example R script uses the quickpeaks function to analyse all the wav files in a directory (selected by choosing any of the wav files in the directory with the file browser).\u00a0 It outputs the results to a csv file in the default R directory.\u00a0 It uses some parameters of quickpeaks to target the first (up to 5) peaks taller than -5 dB.<\/p>\n
# load required libraries
\nlibrary(seewave)
\nlibrary(tuneR)<\/p>\n# first select any wav file in the directory to scan through
\nwavname = file.choose()<\/p>\n# make a list of all the wav files in this directory
\nwavList = list.files(path = dirname(wavname),pattern=”*.wav”,full.names = TRUE)<\/p>\nallresults = -1<\/p>\n
print(wavList)
\ncat(“Found”,length(wavList),”wav files \\n”)<\/p>\nfor(i in 1:length(wavList)){<\/p>\n
cat(“Working on file”,i,”of”,length(wavList),” “,basename(wavList[i]),” \\n”)
\nwav.recorded=readWave(filename=wavList[i])
\nIDuse = basename(wavList[i])<\/p>\nresult = quickpeaks(wav.recorded,
\nplotpeaks = FALSE,
\nplothits = FALSE,
\nmindB=-5,
\npeakscount = 5,
\nID=IDuse)<\/p>\nif(i == 1) {
\nallresults = result
\n} else {
\nallresults = rbind(allresults, result)
\n}
\n}<\/p>\nwrite.csv(allresults, file = “allresults.csv”, row.names=FALSE)<\/p><\/blockquote>\n
<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"