Hi John, I agree with Ian: the first thing to do is to create a separate model which learns the spatiotemporal characteristics of each input metric. This will give you a picture of how well each metric behaves as a measure of the anomalies in your bridge's lifecycle. Experience with Grok (which does only this model-per-metric regime) on numerous systems shows that this is often enough, in that a single high anomaly likelihood score among all the metrics is enough to identify an event worthy of attention, and a second or third blip on other metrics will confirm it.
It's important to use the likelihood score first, as it will filter out many perfectly normal events which your system produces, and which might frequently cause high anomaly scores from the raw predictions. if you can confirm that you are getting good correlations between your known events and likelihood alarms on one or more metrics, this will allow you to identify which single metrics and combinations are best at identifying your disturbances. Once you've identified the clearly best metrics (A, B and C say), you could start adding the others (d, e, f, etc) one at a time, creating a set of metrics which might give you even better correlation (eg Ac, Ba might be better than A or B alone). As Ian says, this is how the swarming algorithm works, but in this case the space of combinations is too large for swarming to make any sense. Use a depth-first approach instead by using single-metric models to group your metrics in quality bands. (The other issue with swarming is that it uses anomaly scores rather than likelihood scores to rank candidate choices of input fields). Please keep us informed about how you get on. Regards, Fergal Byrne On Wed, Aug 13, 2014 at 6:05 PM, Ian Danforth <[email protected]> wrote: > Use separate models for each giving each model time and sensor values. > > Start with two sensors and run both through the swarming process and let > us know what difficulties you run into. > > Ian > On 13 Aug 2014 03:37, "John Blackburn" <[email protected]> wrote: > >> Dear All, >> >> I am a researcher at the National Physical Laboratory, London and am >> attempting to use NuPIC to model the strain and temperature variations of a >> concrete bridge for anomaly detection. The bridge has 10 temperatures >> sensors and 8 "tilt sensors" (basically strain) arranged across it. I have >> hourly readings for all of these sensors for a 3 year period. I would like >> NuPIC to predict all of these quantities (and keep them separate). Compared >> to the "hotgym" example, the difference here is that there are 18 separate >> streams of data which would need to be suitably encoded and decoded to make >> predictions of each one. I suspect the decoding stage would be most >> difficult: from the set of cell activations we need to discover 18 numbers >> and keep them separate. The HTM should account for cross correlations >> between time series as well as auto-correlations. I would like to consider >> +1 and +5 predictions, for example. >> >> During the course of the experiment, various interventions were carried >> out at known times. These include cutting support cables, removing chunks >> of concrete and adding heavy weights. The NN should show anomalous >> behaviour at the time these interventions were done. The system has been >> modelled using an Echo Sensor Network so I want to compare performance of >> ESN to HTM. >> >> So, is this task possible with NuPIC and how might I adjust the encoder, >> decoder to deal with multiple streams? >> >> Many thanks for your help, >> >> John Blackburn. >> >> _______________________________________________ >> nupic mailing list >> [email protected] >> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org >> >> > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > > -- Fergal Byrne, Brenter IT Author, Real Machine Intelligence with Clortex and NuPIC https://leanpub.com/realsmartmachines Speaking on Clortex and HTM/CLA at euroClojure Krakow, June 2014: http://euroclojure.com/2014/ and at LambdaJam Chicago, July 2014: http://www.lambdajam.com http://inbits.com - Better Living through Thoughtful Technology http://ie.linkedin.com/in/fergbyrne/ - https://github.com/fergalbyrne e:[email protected] t:+353 83 4214179 Join the quest for Machine Intelligence at http://numenta.org Formerly of Adnet [email protected] http://www.adnet.ie
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