Predictive Maintenance and Power Continuity
DOI:
https://doi.org/10.5281/esaqkx67Keywords:
Resilience, Digital Twin, Edge Computing, Asset Prognostics, Wide Area Monitoring System, Power Infrastructure,Digital Twins, Edge Computing, Industrial Internet of Things, Asset Prognostics, Predictive Maintenance, Remaining Useful Life, Condition Monitoring, Power Infrastructure Resilience, Edge Intelligence, Fault Diagnosis.Abstract
Climate change is driving the need for investment in resilient power infrastructure. Digital twins offer a promising way to assess the power grid’s ability to withstand and rapidly recover from disruptive events. Building such a digital twin requires combining an asset-prognostics framework—which uses prognostics to detect and predict asset condition—with edge computing to ensure low-latency, high-reliability responses. Automating the development and deployment of such an edge-driven asset-prognostics framework, however, remains an open challenge.
This work presents an industrial digital twin system that meets the requirements of low latency, high reliability, security, and scalability through an edge-driven asset-prognostics framework. These properties enable prediction of asset-indicator time series and of failures that could compromise power infrastructure. The main contribution is the design of the framework itself, encompassing the end-to-end pipeline of data ingestion, pre-processing, feature generation, sensor fusion, and supervised-model training and deployment. The framework supports statistical, machine-learning, deep-learning, and physics-informed methods. Following an applied-science approach, we define suitable metrics to evaluate the performance of each component.
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