Over the last few decades, network domains have become more and more advanced in terms of their size, complexity and level of heterogeneity. Existing centralized-based network management approaches suffer from problems such as insufficient scalability, availability and flexibility, as networks become more distributed. Mobile agents (MA), upgraded with intelligence, can present a reasonable new technology that will help to achieve distributed management. These agents migrate from one node to another, accessing an appropriate subset of Management Information Base (MIB) variables from each node analysing them locally and retaining the results of this analysis during their subsequent migration. One of the network fault management tasks is fault detection, and in this paper our purpose was to carry out a statistical method based on Wiener filter to capture the abnormal changes in the behaviour of the MIB variables. Our algorithm was implemented on data obtained from two different scenarios in the laboratory, with four different fault case studies. The purpose of this is to provide the manager node with a high level of information, such as a set of conclusions or recommendations, rather than large volumes of data relating to each management task. The filtering process is carried out concurrently by each agent responsible for a particular domain and device, proving to be more scalable and efficient.