<p>Intermittent faults are characterized by short duration and random occurrence. They are often missed by machine learning models trained exclusively on permanent fault data. This work proposes a novel physics-informed normalization of spectrograms and unsupervised domain adaptation for efficient diagnosis of intermittent faults using permanent fault data. Domain Adversarial Neural Network (DANN), Wasserstein Distance Guided Representation Learning (WDGRL) and Conditional Domain Adaptation Network (CDAN) are used as the domain adaptation techniques. The approach is validated using a newly developed experimental setup for generating intermittent inter-turn short fault data in a synchronous generator. The results validate the effectiveness of the proposed physics-informed pre-processing and domain adaptation for diagnosing intermittent faults using permanent fault data. Further the results open up possibility of application of various types of domain adaptation techniques to solve many practical problems in the domain of intermittent fault diagnosis</p>