AI Decision Support Prognostics for IoT Asset Health Monitoring, Failure Prediction, Time to Failure

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. We present an advanced pattern recognition system, called the Multivariate State Estimation Technique-2, which possesses functional requirements designed to minimize the possibility of cognitive overload for human operators. These functional requirements include: (1) ultralow false alarm probabilities for all monitored transducers, components, machines, subsystems, 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 prognostic machine learning innovation presented herein does not replace the role of the human in operation of complex engineering systems, but 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.