Abstract: A method, apparatus and computer program product are provided to not only predict an impending fault, but also to avoid the occurrence of the fault such that continued operations are permitted with a reduced likelihood of the occurrence of the fault. In this regard, a plurality of features are provided to at least one model, such as a random forest classification model. The plurality of features include features representative of at least one prior operational sequence as well as features representative of at least one upcoming operational sequence. The plurality of features are then processed with at least one model to determine a likelihood of a fault during the upcoming operational sequence. The method also alters the characteristics of the upcoming operational sequence without requiring maintenance of the equipment to thereby permit the upcoming operational sequence to be conducted with a reduced likelihood of the fault.

Abstract: A method, apparatus and computer program product are provided to predict faults. Initially, a plurality of features are provided to a plurality of models. A subset of features is selected for each model. The plurality of features selected by a respective model is dependent upon a time scale associated with a respective model. As a result of their dependence upon different time scales, the plurality of selected features provided to a first model will differ from those provided to a second model. The plurality of models process the respective plurality of selected features. The outputs from the plurality of models are fused to generate a measure indicative of an impending fault. By providing different selected features to the models that are dependent upon the associated time scales and by then combining the outputs of the plurality of models, the resulting measure of an impending fault may accurately predict a fault well in advance of its occurrence.

Abstract: A method, apparatus and computer program product are provided to not only predict an impending fault, but also to avoid the occurrence of the fault such that continued operations are permitted with a reduced likelihood of the occurrence of the fault. In this regard, a plurality of features are provided to at least one model, such as a random forest classification model. The plurality of features include features representative of at least one prior operational sequence as well as features representative of at least one upcoming operational sequence. The plurality of features are then processed with at least one model to determine a likelihood of a fault during the upcoming operational sequence. The method also alters the characteristics of the upcoming operational sequence without requiring maintenance of the equipment to thereby permit the upcoming operational sequence to be conducted with a reduced likelihood of the fault.

Abstract: A method, apparatus and computer program product are provided to predict faults. Initially, a plurality of features are provided to a plurality of models. A subset of features is selected for each model. The plurality of features selected by a respective model is dependent upon a time scale associated with a respective model. As a result of their dependence upon different time scales, the plurality of selected features provided to a first model will differ from those provided to a second model. The plurality of models process the respective plurality of selected features. The outputs from the plurality of models are fused to generate a measure indicative of an impending fault. By providing different selected features to the models that are dependent upon the associated time scales and by then combining the outputs of the plurality of models, the resulting measure of an impending fault may accurately predict a fault well in advance of its occurrence.

Abstract: A method and system for performing model-based multi-objective asset optimization and decision-making is provided. The method includes building at least two predictive models for an asset. The building includes categorizing operational historical data via at least one of: controllable variables, uncontrollable variables, output objectives, and constraints. The building also includes selecting at least two output objectives or constraints, and identifying at least one controllable or uncontrollable variable suitable for achieving the at least two output objectives or constraints. The method also includes validating each predictive model and performing multi-objective optimization using the predictive models. The multi-objective optimization includes specifying search constraints and applying a multi-objective optimization algorithm. The method further includes generating a Pareto Frontier, and selecting a Pareto optimal input-output vector.