Data-driven predictive maintenance reduces manufacturing downtime, and complex process-sensing relationships encourage the use of deep learning to automatically extract features. However, labeled training data are often lacking, and novel fault conditions may occur. Practical deployments must learn from unlabeled data, adapt to emerging conditions, and do so without prior knowledge of when the condition changes. Combining state-of-the-art self-supervised learning (SSL) with continual learning (CL) facilitates adaptation as new conditions are observed. This article proposes a framework for adaptive online condition monitoring based on Barlow Twins SSL and novel Mixed-Up Experience Replay (MixER) for unsupervised CL. Tailored for 1-D sensing data, Barlow Twins effectively clusters unlabeled data. When combined with MixER, the system outperforms state-of-the-art unsupervised CL on a motor health condition dataset, reaching 92.4% classification accuracy. Future work will demonstrate human-in-the-loop integration for real manufacturing environments.