The complexity of technical systems requires increasingly advanced fault diagnosis methods to improve safety and reliability. Particularly in domains where maintenance poses an extensive part of the entire operation cost, accurate identification of failure sources has a large economic impact. Model-based diagnosis, as a subfield of Artificial Intelligence, allows to determine root causes based on observed anomalies and has already been applied to a variety of domains. Abductive model-based diagnosis considers information on failures and how they affect detectable system measurements. Thus, this type of fault localization procedure depends on systematic and analytic knowledge on components, their possible malfunctions, and the subsequent effects. In this paper, we examine various common failure assessments such as Failure Mode Effect Analysis, in regard to serving as a basis for abductive diagnosis. In particular, we analyze the methods concerning their advantages and limitations as sources of failure information within our diagnosis process.