Predicting time-to-failure using multiple failure datasets and time-series sensors data 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?

Classically Survival Analysis and time-to-even models are suitable. Matlab has a tutorial introduction to Predictive Maintenance which also introduces Remaining Useful Life (RUL) models. More modern approaches like Adversarial Time-to-Event Modelling can also be used.
