Performing decentralized network fault diagnosis based on network traffic is challenging. Besides inherent stochastic behaviour of observations, measurements may be subject to errors degrading diagnosis timeliness and accuracy. In this paper we present a novel approach in which we aim to mitigate issues of measurement errors by quantifying uncertainty. The uncertainty information is applied in the diagnostic component to improve its robustness. Three diagnosis components have been proposed based on the Hidden Markov Model formalism: (H0) representing a classical approach, (H1) a static compensation of (H0) to uncertainties and (H2) dynamically adapting diagnosis to uncertainty information. From uncertainty injection scenarios of added measurement noise we demonstrate how using uncertainty information can provide a structured approach of improving diagnosis.