Edge-Native Cognitive Prognostics for Resilient Fleet Power Operations
Keywords:
Edge-enabled fleet power operations, cognitive asset intelligence, prognostics, asset resiliency, real-time anomaly detection, time-series analysis.Abstract
A leading framework for the intelligent, asset-driven circular economy is the Industrial Internet Consortium’s “Cognitive Industrial Asset Intelligence Framework” (CI4AI). This white paper examines its application to edge-enabled, AI-based fleet risk assessment tools designed for time-to-failure estimation, prognosis, and the management of power operations—capabilities that, while still uneven in coverage, are critical to assessing the feasibility and provisioning of future operations.
The CI4AI consortium proposes a three-layer architecture: a Data layer (covering ingestion, preparation, storage, and management), an Analytic Algorithms layer (built on data lakes, data meshes, and Cognitive AI foundations for Causal-Trees), and an Asset Intelligence Modelling layer. Assets operating within this architecture are exposed to cyber, geographic, physical, and other domain-specific risks. However, a gap remains: knowledge-based algorithmic tools for quantitative, computerized risk assessment of time-to-failure events—particularly those integrating spatio-temporal, pattern-based and pattern-enhancing prognosis—are still largely absent.
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