This paper compares two fault identification implementations based on a neural network and a model based approach. Our worked example is the detection of gas bubbles in the pump head of a centrifugal blood pump. We focus on algorithms applicable on minimal sensor data with a reasonable implementation effort. The approaches were restricted to the desired blood flow and the measured rotational speed of the pump. We evaluated both implementations with data from an ECMO system.