Working with few instances of specific target feature over large dataset
When you lack data you can use some theoretical knowledge on what should happen before the failure. For example for bearings a vibration in the frequency of the bearing should rise exponentially. The same goes for temperature, but there are too many variables that influence temperatures and it is not that easy to separate it. There are temperature sensors in some bearings. That should be more precise. Microphones can hear the problems like humans do. Ultrasound tools allow humans to hear the problems earlier. 

You can read about some of these patterns in An introduction to predictive maintenance by R. Keith Mobley