I am asking this with the hope this question can be helpful for me, and for others in my same situation.
I am working for a Company. We mounted some sensors on an industial machine, in order to get some data about its state in working moments (between 3 and 10 minutes), like oil pressure and temperature (taken at frequency x), or other vibrational data (this machine has some mechanical arms; these data are taken at frequency y, with y >>> x).
Now, I am supposed to retrieve these data (in form of Univariate Time Series), and empirically apply some Anomaly Detection Algorithm. Our final aim will be to apply some online mechanism on online data stream for instant detection. For now, just for the first period, I can apply some batch technique.
My question is: given all I said, which technique/algorithm is more suitable to the problem? I've seen almost all of it (Twitter's, Netflix's RAD, Farrington, all Time Series Analysis packages for R, and so on), but I cannot figure out which should I apply to get the best results. Most of them find as an outlier high peaks, for example in vibrational data, that are instead normal operations, relative to some product change or operation resets.
A nice approach I found, is the one based on SAX and bitmap image, and assumption-free, by Keogh and others (http://alumni.cs.ucr.edu/~ratana/KumarN.pdf). I found it easy and intuitive, but I have a feeling it's not what I am looking for.
I am not an expert about what this machine does, so I cannot figure out, by reading the data, what operation is it doing, or label an operation as normal or anomalous. So, an unsupervised approach is preferable.
I am using R and RStudio for the data analysis.