Time series anomaly detection

Question

I would like to get suggestions about a time series problem. The data is about strain gauge on the wing of flight which is measured using different sensors. Basically, we are creating the anomalies by simulating the physics model. We have a baseline which is working fine and then created some anomalies by changing some of the factors and recorded over time. Our aim is to create a model which can find out the anomaly during the live testing(it can be a crack on the wing), basically a real time anomaly detection using statistical methods or machine learning.

Answer 1

A few thoughts - sorted roughly from top-to-bottom based on time investiment (assuming little/no prior ML knowledge):

• start simple and validate: for what you've described this could be as simple as

  • create a training / validation dataset using your simulator - since you can simulate, do so for significant episodes of both "standard" and extreme forces applied to the wing

  • choose a real time smoother: e.g., exponential averaging or moving average, determine a proper parameter for each of your input sensor signals. smooth the input signals.

  • determine threshold values:

      - create rough but sensible lower bound threshold values "by eye"
    
      - use simple statistics to determine a decent threshold value (e.g., using a moving fixed length window of appropriate size, and setting the threshold at a multiple of the standard deviation in that window slid across the entire signal)
    

  • in either case, testing on further simulated (and - ideally also - real data)

If an effort like this works "good enough" - stop and move on to next (facet of) problem. If not

• follow the first two steps (simulate and smooth data)

• take an "autoregressive" approach create training / validation input/output pairs by running a sliding window of fixed length over the input signal(s). train a simple supervised learner on thes pairs, for each input signal or all together, to produce a (set of) time series anamoly detectors trained on your simulated data. cross-validate with the validation portion of your data.

  • use this model (or one like it) on your validation data to test performance - and ideall collect real data (not simulated) to validate your model even further on.

If this sort of approach produces "good enough" results - stop, and move onto the next facet of the problem.

If not - examine and try any number of anomoly detection approaches coded in a variety languages listed on an aggregator like the awesome repo for time series anomaly detection