How should I encode 'dynamic' features (with multiple instances) along with 'static' features (single instances)?

Suppose I have to predict if a certain product from an assembly line in a factory will be a scrap. This product has let’s say ‘static’ data like a certain shape. A certain vendor, etc. And, it can have ‘dynamic’ data this meaning it can have for example: one or more sets of measurements (pressures,temperatures ,etc) from production processes.

How to treat this ‘dynamic’ features ?

Somehow it doesn’t seem right to repeat the ‘static’ data for all ‘dynamic’ events. And using the mean of ‘dynamic’ features would dilute the information.

I’m thinking to encode this ‘dynamic’ data in a similar way phrases with variable number of words are encoded in fix length vectors with lstm networks. What do you think ?