Self-Learning Health Assurance for Connected Fleets
DOI:
https://doi.org/10.5281/9ppfj860Keywords:
Predictive analytics; operational resilience; industrial systems; prognostic health management; Internet of Things; asset health; connected systems; machine learning; artificial intelligence; autonomy; safety; governance; security.Abstract
This research presents a framework for asset health and fleet resilience in connected IoT environments, enabled by machine intelligence and predictive analytics. The framework combines analytical methods with a system-level architecture for service delivery, aiming to deliver and validate predictive maintenance analytics at scale. It addresses the interests of OEMs, Industry 4.0 initiatives, and sustainability goals framed around Environmental, Social, and Governance (ESG) objectives. Its domain spans asset health, predictive maintenance, and resilient fleet operations, with a resilience perspective centered on governing predictive maintenance practices, calibrating organizational risk tolerance, and balancing autonomy against human oversight. Data-driven analytics, alongside distributed data management and governance, are most salient in the IoT and Sensor Data Management research areas. The hypotheses and evaluation criteria presented here draw on both literature-based guidance and lens-based reasoning.
Asset health predictive analytics are best evaluated through successful project delivery; at scale, this requires embedding analytics within a production service that problem owners and users can discover and adopt. A systematic approach to service delivery therefore accelerates project realization and broadens coverage. In mission-critical settings, service consumption becomes mandatory, meaning the acceptable risk balance is effectively determined by the service itself. The development team functions as the gatekeeping authority for service quality, shaping analytics, shadow data processing, and incident detection. Within this structure, knowledge gaps are closed by selecting appropriate predictive maintenance methods, service consumption enables validation testing, and security requirements govern shadow data processing.
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