A fault diagnosis model may be degraded or may fluctuate in an on-site refrigeration system. Thus

the model needs to adaptively learn the on-site data. To learn the online data stream of a refrigeration system during fault diagnosis

an adaptive diagnosis model is developed using the accurate threshold-based concept drift detection mechanism and an incremental support vector machine algorithm

which are applied to re-learn the refrigerant overcharge failure. Using two optimization processes to select and filter the data information

the algorithm retains the original diagnostic knowledge and only learns the unknown sample data information

which can greatly save training time and quickly adapt to a new environment. Simulation experiments are performed for online learning of the diagnosis of the refrigerant overcharge failure. The results show that when a new type of fault occurs

the diagnostic model detects the concept drift and then updates itself through incremental learning to learn and diagnose new faults. Three concept drifts are detected

and the diagnosis model only needs to update three times to realize learning of the refrigerant overcharge failure

that is

the diagnostic model only learns 600 of the 1400 refrigerant overcharge failure samples and ensures that the ultimate diagnosis model achieves better diagnostic performance for subsequent data streams with the correct rate reaching over 99%. In terms of on-site refrigeration system fault diagnosis application

the re-learning and self-adaptation of the diagnostic model shows good application potential.