Training machine learning models from log files
Hi and welcome to the community!

Sure you can use ML. I would start with brainstorming to formulate the problem. So one idea could be Sequence Learning:


Injecting a unique and recognizable number instead of F and training a NN-based model (RNNs are used in this case e.g. ESNs or LSTMs) will learn the dynamic of the data themselves as they model memory. But for training such models you need lots of data actually (to be more precise, you need long Time-Series). In this solution, the task is to see that unique and recognizable number in the prediction and output is a failure. See this for more info.
Hidden Markov Models are also designed for such tasks.


Another idea could be:


Segment each time series into intervals ending in a failure. For example:


  ts1 = [1,2,1,2,F,1,2,3,4,F1,2,F]



Becomes

&gt; ts1_1 = [1,2,1,2]
&gt; ts1_2 = [1,2,3,4]
&gt; ts1_3 = [2]


Now you have a set of training samples (positive samples only!) for Time-Series 1 (e.g. Temperature). Now produce some negative samples from those sequence segments which do not result in "F" and train a model for e.g. classifying. Then on real-time prediction, you need to make a prediction after receiving every new point (temp for instance) and see if it is going to result in a failure. 

DISCLAIMER: This way is not what you really do in practice but a nice starting idea for a newbie learner in the field!

I recommend having a look at this answer, this tutorial and this video as well.

Hope it helps!