Fault diagnosis in telecommunication networks requires extensive expert knowledge and is key to efficient network operations and high service availability. Specifically, discovering and identifying new faults occurring in the network is a challenging task. Some dominant methods in industry are based on expert systems or Bayesian networks. Both of these methods require considerable expert knowledge and time resources to construct and maintain the diagnosis system. In this paper, we propose a data driven approach for the clustering and identification of new faults, based on existing knowledge, using neural networks and infinite mixture models. In our approach deep infinite mixture models are capable of extracting interesting features from labeled data, which are then leveraged in the clustering process to identify new relevant faults in unlabeled data. We apply our method to real operational data from Fiber-to-the Home services based on Gigabit-capable Passive Optical Networks. We show that our approach can be trained end-to-end, and allows to identify and interpret new faults. INDEX TERMS Network fault diagnosis, deep learning, infinite mixture models, variational inference.