Scale-Aware Fault Intelligence for Electrochemical Energy Systems

Abstract

This review examines scale-aware learning and two-stage fault intelligence for electrochemical energy systems through a mechanism-to-decision framework. It asks how multiscale losses and operational fault classes should be balanced. Evidence is organized around representation, validation, uncertainty, and operational control, with no invented experiments or unreported quantitative results. Particular attention is given to majority operating states suppressing rare degradation signals. The synthesis shows that credible translation depends on explicit system boundaries, source-level traceability, failure-aware evaluation, and revalidation triggers. These principles provide a disciplined basis for fuel-cell and wind-energy diagnostics while keeping component promise distinct from system readiness.