Explainable Artificial Intelligence for Risk Assessment and Decision Support in Industrial Equipment Management
Keywords:
explainable artificial intelligence; industrial equipment management; risk assessment; decision support; predictive maintenance; algorithmic governanceAbstract
The increasing digitization of industrial equipment has made machine learning a central element of risk assessment and maintenance decision support. At the same time, the opacity of many high-performing models creates tensions between predictive accuracy and operational accountability. This paper examines explainable artificial intelligence as a system-level capability rather than a set of isolated post hoc algorithms. It argues that the value of explainability in industrial equipment management emerges from the interaction among data infrastructure, model architecture, organizational decision processes, regulatory requirements, and sustainability objectives. The discussion addresses structural trade-offs between model complexity and interpretability, centralized and edge-based deployment, automation and human oversight, and explanation fidelity and operational latency. It further considers data governance, model monitoring, robustness under distribution shift, fairness across asset fleets, and policy implications under emerging artificial intelligence regulations. The paper uses cross-domain illustrations from wind energy, rail, process industries, and manufacturing to show how explainable risk assessment can support maintenance planning, resource allocation, and safety assurance. Rather than treating explainability as a purely technical property, the analysis highlights the importance of institutional routines, auditability, and socio-technical alignment. The conclusion outlines directions for future research on integrated explainability in industrial decision support systems.
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