Abstract: An apparatus, system and method of providing industrial analytics. The apparatus, system and method include at least a plurality of sensors sensing performance indicators for an industrial process across multiple nodes, wherein ones of the multiple nodes are remote from each other; at least one machine learning module comprising non-transitory computing code executed by a processor. When executed by the processor, the code causes the steps of: receiving user input regarding at least the industrial process and a data set; selecting a model based on at least the user input, wherein the selected model comprises a plurality of learnings based on the performance indicators sensed by multiple sensors across at least multiple ones of the multiple nodes; applying the selected model to the data set; assessing at least the performance indicators for the data set upon application of the selected model; and outputting the assessed performance indicator.

Abstract: A machine learning based method for determining a likelihood of corrosion of a component is provided. The method comprises receiving data associated with a portion of at least one component, the data describing one or more operating conditions of the portion of the at least one component, applying, to the data associated with the portion, a first machine learning model, determining, responsive to the applying of the first machine learning model, a likelihood of corrosion specific to the at least one component based at least in part on the one or more operating conditions of the portion, and outputting, automatically and without user intervention, the likelihood of corrosion specific to the at least one component on a display.

Abstract: An apparatus, system and method of providing industrial analytics. The apparatus, system and method include at least a plurality of sensors sensing performance indicators for an industrial process across multiple nodes, wherein ones of the multiple nodes are remote from each other; at least one machine learning module comprising non-transitory computing code executed by a processor. When executed by the processor, the code causes the steps of: receiving user input regarding at least the industrial process and a data set; selecting a model based on at least the user input, wherein the selected model comprises a plurality of learnings based on the performance indicators sensed by multiple sensors across at least multiple ones of the multiple nodes; applying the selected model to the data set; assessing at least the performance indicators for the data set upon application of the selected model; and outputting the assessed performance indicator.