Abstract: A non-transitory computer readable medium (107, 127) stores: a predictive model (130) configured to generate an alert (132) predicting a failure of a component of a medical imaging device (120) by applying patterns to values of a set of features; a table (136) having records corresponding to the patterns of the predictive model; and instructions readable and executable by at least one electronic processor (101, 113) to (i) train: a sequence model (134) to receive values of the set of features for a current case and to output a most probable root cause and at least one service action for the current case, and (ii) determine a root cause and at least one recommended service action for the alert generated by the predictive model by applying the trained sequence model to the values of the set of features for the medical imaging device.

Abstract: According to an aspect there is provided a computer-implemented method of monitoring performance of a predictive computer-implemented model, PCIM, that is used to monitor the status of a first system. The PCIM receives as inputs observed values for a plurality of features relating to the first system, and the PCIM determines whether to issue status alerts based on the observed values.

Abstract: A predictive maintenance alerting device (40) comprises a server computer (42) operatively connected with an electronic in network (46) to receive time stamped machine log data (30) and time stamped service log data (32) from a medical imaging device (10), and to transmit maintenance alerts (44) to a service center (12). The predictive maintenance alerting method includes deriving features from the received log data, and applying a set of models (64) of component groups to the derived features to generate the maintenance alerts. Each model may comprise a heterogeneous model including a machine learned analytical model (70) representing the component group with embedded statistical remaining useful lifetime models (72) of the components of the component group. Each component may belong to a single component group. Some derived features may be built failure mode features or built failure resolution features.