Predictive analysis of Rotating Equipment

I’m looking to develop a model to predict the Remaining Useful Life (RUL) and detect anomalies in rotating equipment faults. I have two years of healthy data for each asset, but no faulty data is available. I have a fault model based on human judgment but want to create a model that can take live timestamp data as input and detect faults, using the provided fault models.

For example, consider a centrifugal pump with overhung construction. I have timestamped data every 2 minutes for the past two years, including the following sensors: - Bearing vibration - Bearing temperature - Motor current - Suction tank level-pressure - Suction filter delta pressure

I’ve established conditions such as: - If suction level is decreasing and pump vibration is increasing, then cavitation may be occurring.

How can I build a model based on these conditions without having faulty data for training? I would appreciate any expert advice or experiences you can share.