I have a dataset of a machine for the past year. The dataset consists of timestamps, various sensors readings, and machine failures. Different sensors have different data recording intervals (some recorded every 5 mins, some recorded every 30 mins), so the timestamps are different for each sensor (temperature, humidity, vibration, etc). A sample dataset (sensors with their respective timestamps) structure is as follows:
There are six failure events during one year time. Each failure event is provided with its timestamp. I want to train a machine learning model using these data and predict future failure occurrences in advance using the streaming data from sensors. My situation is very close to this and this questions. Which ML model will give me a good prediction result? How can I use the failure events as a target in my ML model?