Closed-Loop Cognition for Resilient Industrial Ecosystems
DOI:
https://doi.org/10.5281/n2ry9f80Keywords:
Cognitive network, communication technology, causation, high-dimensional data analysis, information technology, industrial systems, machine learning, resilient systems, uncertainty quantification.Abstract
Persistent pressure to reduce costs, coupled with growing data availability, has driven substantial investment in data-driven decision support. Although many solutions touch on predictive maintenance of physical assets, few have translated into systems of systems capable of genuine time-fault-tolerant operation. This paper proposes a paradigm for predictive health management of industrial systems that are nondeterministic and observed but not directly controlled.
We introduce Autonomous Cognitive Infrastructure (ACI), a framework that enables data-intensive causal inference, uncertainty quantification, and predictive maintenance by combining emerging AI-based methods with principles from decision theory. ACI functions as a predictive cognitive cell, structured around layered healthy-state and asset-health indicators. It provides three core capabilities: (1) a scalable, continuously expanding supply of asset health indicators; (2) a native, demand-side energy requirement model with the temporal resolution needed to support core energy services; and (3) uncertainty quantification that strengthens the resilience of distribution and supply functions, with support absorbed by local Recovery Maintenance Organizations.
For an observed-but-not-controlled lateral power system, we define a single primary health indicator — ideally the only one required — that aggregates the health status of critical components through three measures: the Root of the Health Function, the Root Area Index, and the Root Performance Assessment. Three additional sub-models govern the remaining energy flows within the lateral power system and draw on the services provided by the predictive maintenance solution.
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