Failure prediction from sensor data using Machine Learning
Question: I am going to do a research project which involves predicting imminent failure
of an engine using time data obtained from sensors. The data basically
contains the readings of various embedded sensors every 10 minutes for many
months. Such data is available for about 100 or so different units (all are
the same engine model), along with the time of failure.

While I do have a reasonably good understanding of Machine Learning, I am at a
loss of approaching this. I have done a few projects that involved static
datasets (using SVMs, Neural Nets, Logistic Regression etc.) and even one on
predicting time series. But this is quite different. While the project
involves time data, it is hardly a matter of predicting the future values.
Rather it is a case of anomaly detection on sequential time data.

Please could you give some ideas as to how I could approach it? I'm
particularly interested in Neural Networks/ Deep Learning, so any ideas on
using them for this task would also be welcome. I would prefer to use Python
or R, although I would be open to using something else if it was particularly
geared for this sort of task. Also could you give me some formal terms using
which I could search for relevant literature?

Thanks


Answer: As a general comment, try hard to express everything that you know about the
physical system in a model, then use that model for inference. I worked on
such problems in my dissertation: [Unified Prediction and Diagnosis in
Engineering Systems by means of Distributed Belief
Networks](http://riso.sourceforge.net/docs/dodier-dissertation.pdf) (see
chapter 6). I can say more if you provide additional details about your
problem domain.

Don't expect general machine learning models (neural networks, SVM, etc) to
figure out the structure of the problem for you. Having the right form of the
model is much, much more important than having a general model + lots of data
-- this is the summary of my experience.

