Industrial and infrastructure systems increasingly operate in dynamic environments characterised by fluctuating loads, changing operating modes, and varying environmental conditions. These non-stationary behaviours challenge conventional condition monitoring and fault diagnosis approaches, which typically rely on static models, fixed thresholds, or pre-defined fault signatures. As operating conditions evolve, such approaches often suffer from reduced diagnostic accuracy and increased false alarm rates. Self-learning condition monitoring systems offer a promising pathway toward adaptive and resilient fault diagnosis in such dynamic operating environments. This study proposes a self-learning condition monitoring framework that continuously adapts to changing system behaviour through online and incremental learning mechanisms. The framework integrates multi-sensor data streams with adaptive machine learning techniques capable of updating feature representations, decision boundaries, and fault models in real time. By combining unsupervised anomaly detection with semi-supervised learning, the system can identify emerging degradation patterns and previously unseen fault modes while maintaining robustness to normal operational variability. A key contribution of the proposed approach is its ability to differentiate between benign operational changes and true fault-related anomalies. Context-aware feature extraction and adaptive thresholding are employed to reduce false alarms under varying load and environmental conditions. Feedback loops are incorporated to refine diagnostic confidence and improve model performance as new data become available, enabling the system to learn continuously from operational experience. Illustrative case studies demonstrate that the self-learning framework achieves improved fault detection accuracy, enhanced adaptability, and lower false alarm rates compared to traditional static monitoring methods. Overall, this work highlights the potential of self-learning condition monitoring systems as a foundation for intelligent, autonomous fault diagnosis, supporting reliable and efficient operation of complex assets in highly dynamic operating environments.