  Model based diagnosis finds a growing range of practical applications, and
significant performance-wise improvements have been achieved in recent years.
Some of these improvements result from formulating the problem with maximum
satisfiability (MaxSAT). Whereas recent work focuses on analyzing failing
observations separately, it is also the case that in practical settings there
may exist many failing observations. This paper first investigates the
drawbacks of analyzing failing observations separately. It then shows that
existing solutions do not scale for large systems. Finally, the paper proposes
a novel approach for diagnosing systems with many failing observations. The
proposed approach is based on implicit hitting sets and so is tightly related
with the original seminal work on model based diagnosis. The experimental
results demonstrate not only the importance of analyzing multiple observations
simultaneously, but also the significance of the implicit hitting set approach.
