Unexpected breakdowns in manufacturing occur frequently, resulting in periods of downtime and potential revenue losses. Recent research advancements apply machine learning to predict the occurrence of these breakdowns. Based on the collection of historical data describing the circumstances before these malfunctions appear, models are trained to find patterns in the data that predict the remaining life time of a machine. The main drawback of these approaches is however their reliance on (lots of) data to be able to function well for many types of failures and operating conditions. Therefore, in this research proposal, I aim to guide these models to the right solution by also providing them with physical knowledge available about the machine. Engineers often have detailed knowledge about the physical behavior of the machines they design. For example, when a car engine is running, it should not exceed certain temperatures or noise levels. When it does, something is wrong with the engine, meaning that it will fail soon. Instead of having the models to learn these known patterns from historical data, we will provide them with this expert knowledge from the beginning. This enables them to learn much faster and with less data. In addition, we don't want to retrain these programs for every new environment a machine is deployed in (e.g., different weather conditions). The models should be smart enough to extrapolate important warning signs they have learned from other environments.