Yes, DiagFit is robust to context changes as long as the data has been seen in training. In addition, our feedback functionality makes it possible to enrich the models previously built. When a new anomaly appears, the user has the option of approving or rejecting this anomaly: If it is a real anomaly, the user can label it and thus enrich a dictionary of failures. DiagFit is able to calculate the probability of its belonging to one or more categories of failures already seen. It then gives the user a confidence score with regard to this labelling. If it is a false anomaly (or false positive), the user can reject it and re-train the model taking this behavior into account More about the failure dictionary