@MahmoudR there could be three ways to go forward.
You could state your problem as a classification task with 1/0 or true/false. Then all prediction models for classification are open to you. You could start with a decision tree that can easily be interpreted and you could take your rules from there. Although it might not be the strongest model.
And move on to random forest and xgboost models. Or you could let some auto machine learning do the task.
If models get very complicated they become harder to be interpreted. You might be able to employ a model but only know so much of its inner workings besides variable importance. Methods like LIME could help you with that
https://hub.knime.com/search?q=Lime&type=Workflow
Another approach might be to use rule induction methods. They might give you chains or sequences of events sometimes similar to a decision tree. They might have a target. I put together some in a workflow - you would have to put in some work with configuration and interpretation.
Next attempt could be to formulate your task as a regression problem. % of failure or an index. In this case the world of regression models is open to you. I just reference my recent post about interpreting an automl approach again.