Best algorithm for classifying time series motor data I am working on a machine control project.  We can measure the motor's current during operation.  Sample data from two motors performing an operation successfully is below.  The red trace shows the current from one motor, the blue trace the current from another.  I'd like to try and come up with an algorithm for identifying problems with machine behavior.  Problems could be excessively high motor current, near zero motor current, current increasing at the end of the operation, a shorter time series than normal, anything in general which doesn't look like a typical operation below.  Can anyone suggest a good algorithm for achieving this?  The only one I'm familiar with is a neural network.  I have put an Excel file of actual data at motor currents


 A: My approach is to form an ARIMA Model for the data and then to employ various "change-point detection schemes" in order to provide early warning about unexpected "things". These schemes would include 


*

*detecting the presence/onset of Pulses/Level Shifts/Local Time Trends i.e. changes in the mean of the errors over time

*detecting the presence/onset of changes in parameters over time

*detecting the presence/onset of changes in variance of residuals over time


If you wish to actually post one of your series we could actually show you this kind of analysis which can "push out" the idea that things are changing or have changed significantly.
A: I would suggest you this link that deals with time series classification: http://www.r-bloggers.com/time-series-analysis-and-mining-with-r/.
A: Hidden Markov Model
One of the best approaches to modeling time series data is a Hidden Markov Model (HMM). You can either make a single model of your know non-problem state, separate models of each of your known problem states or, if you have sufficient data, a single composite model of all of your known problem states. A good open source library is the Hidden Markov Model Toolbox for Matlab.
http://www.cs.ubc.ca/~murphyk/Software/HMM/hmm.html
Kalman Filter
Another approach that is a little more involved is a Kalman Filter. This approach is especially useful if your data has a lot of noise. A good open source library is the Kalman Filter Toolbox for Matlab.
http://www.cs.ubc.ca/~murphyk/Software/Kalman/kalman.html
Bayesian Models
Both of these approaches are considered Bayesian Models. A good open source library is the Bayes Net Toolbox for Matlab.
http://code.google.com/p/bnt
I hope this works for you.
