A supervisory control loop with Prognostics for human-in-the-loop decision support and control applications

Abstract

This paper presents a novel tandem human-machine cognition approach for human-in-the-loop control of complex business-critical and mission-critical systems and processes that are monitored by Internet-of-Things (IoT) sensor networks and where it is of utmost importance to mitigate and avoid cognitive overload situations for the human operators. The approach is based on a decision making supervisory loop for situation awareness and control combined with a machine learning technique that is especially well suited to this control problem. The goal is to achieve a number of functional requirements: (1) ultra-low false alarm probabilities for all monitored transducers, components, machines, systems, and processes; (2) fastest mathematically possible decisions regarding the incipience or onset of anomalies in noisy process metrics; and (3) the ability to unambiguously differentiate between sensor degradation events and degradation in the systems/processes under surveillance. The novel approach that is presented here does not replace the role of the human in operation of complex engineering systems and processes, but rather augments that role in a manner that minimizes cognitive overload by very rapidly processing, interpreting, and displaying final diagnostic and prognostic information to the human operator in a prioritized format that is readily perceived and comprehended.