Online fault diagnosis is crucial for ensuring the safe and efficient operation of industrial processes, particularly in real-world scenarios where operation changes and new unlabeled anomaly data are continuously introduced. However, conventional batch learning and incremental learning (IL) methods rely on historical or labeled data, limiting their diagnostic performance in such dynamic conditions. To address this challenge, we propose a novel unsupervised IL framework that detects unseen faults and updates the model in real time. This approach combines out-of-distribution detection with an IL technique to handle unlabeled new data for online fault diagnosis. Out-of-distribution detection in neural networks detects new classes in the data and assigns labels accordingly. Subsequently, IL with knowledge distillation is used to continuously update the model, incorporating the newly labeled data. A transformer-based classifier, chosen for its high predictive performance and adaptability, serves as the pretrained model, facilitating real-time updates. The framework’s efficacy and accuracy in updating the model for real-world scenarios with unlabeled new data were validated using the Tennessee Eastman process and the CWRU bearing data set, demonstrating significant improvements.