Abstract: A system and method including receiving a request to remove a contribution of a targeted sensor data set from a construction of a model. The targeted sensor data set is from a plurality of sensor data sets, each of the plurality of sensor data sets is associated with sensors monitoring industrial systems, the model is constructed at least in part from the plurality of sensor data sets, and each of the plurality of sensor data sets is tagged with at least one unique accession identifier. An accession identifier associated with the targeted sensor data set is then identified, and the construction of the model is modified to remove the contribution of the targeted sensor data set based at least in part on identifying the accession identifier associated with the targeted sensor data set.

Abstract: Methods and accompanying systems are provided for predicting outcomes, such as industrial asset failures, in heavy industries. The predicted outcomes can be used by owners and operators of oil rigs, mines, factories, and other operational sites to identify potential failures and take preventive and/or remedial action with respect to industrial assets. In one implementation, historical data associated with a plurality of outcomes is received at one or more central site servers from one or more data sources, and datasets are generated from the historical data. Using the datasets, a set of models is trained to predict an outcome. A particular model includes sub-models corresponding to a hierarchy of components of an industrial asset. The set of models is combined into an ensemble model, which is transmitted to remote sites.