Adaptive distributed analytics system

#The present invention is directed, in general, to control systems and, more particularly, to predictive and prescriptive analytics systems.

#Industrial systems are increasingly complex including the protocols to manage the operations thereof. In an effort to characterize, improve and optimize the operation of industrial systems, organizations seek to collect as much data as possible from systems and subsystems. The proliferation of sensors (e.g., devices that monitor one or more physical characteristics) allows organizations to quantify the state of a system and, potentially, adjust operation of the system to improve performance. The data collected through the sensors, however, is not inherently actionable and requires some level of data analysis, human interpretation, or a combination thereof to be useful.

#Current approaches in industrial systems analysis typically include networked connection of sensors to a central server or cloud-based network. Within the aggregate collection of data, machine learning or artificial intelligence techniques are applied to multi-dimensional datasets to quantify the correlation between measured system data and process performance outcomes. The feedback loop affecting operation of the industrial systems may occur in real-time depending on the connectivity of the system, the processing speed of the data analysis procedures, and the process rate of the underlying system.

#For a more complete understanding of the present invention and the advantages thereof, reference is now made to the following descriptions taken in connection with the accompanying drawings, and which:

#FIG. 1 illustrates a block diagram of an embodiment of a communication system 100;

#FIG. 2 illustrates a block diagram of an embodiment of process subsystems employable in a communication system;

#Corresponding numerals and symbols in the different figures generally refer to corresponding parts unless otherwise indicated, and may not be redescribed in the interest of brevity after the first instance. The FIGUREs are drawn to illustrate the relevant aspects of exemplary embodiments.

#The making and using of the present exemplary embodiments are discussed in detail below. It should be appreciated, however, that the embodiments provide many applicable inventive concepts that can be embodied in a wide variety of specific contexts. The specific embodiments discussed are merely illustrative of specific ways to make and use the systems, subsystems, and modules associated with a system and method that provides predictive and prescriptive analytics for applications. It should be understood that the term “system” refers in some instances to the “system” or “entity” (or portions or components thereof) being monitored (a “monitored system”) and in other instances the term refers to the “system” performing the predictive and prescriptive analytics therefor.

#A system will be described herein with respect to exemplary embodiments in a specific context, namely, a broad class of industrial systems and subsystems. The specific embodiments of industrial systems include, but are not limited to, motor systems, pump systems, motor-pump systems, hydraulic systems, actuator systems, and drilling systems. The principles of the present invention are applicable to operational maintenance and support in many fields including, but not limited to, manufacturing, maintenance, supply chain, inventory, electricity generation, oil and gas, and aviation. The specific embodied approach below is referred to as the AnalyticsOS (“AOS”) platform including multiple components.