Events prediction with time series of continuous variables as features
First of all, you will not be able to prove anything with a model, you will have false positives/negatives. With a good model you may be able show what variables may be an indicator of component failure. 

In problems like this feature generation can have the most important influence on the accuracy of the model. The time stamps can be used for aggregation. For example, you may aggregate metrics per device per hour. The metrics/features that you might create for input into the model might be average/max temperature or fan speed, rate of change in temperature or fan speed, number of seconds device was above some threshold temperature or fan speed, boolean indicator of voltage spike, etc. There could be any number of features you may create. You can then find which features are not strong predictors and remove these columns to reduce noise, if need be.