Data-Driven Differential Invariant Learning for Intelligent Fault Diagnosis and Remaining Useful Life Prediction in Industrial Systems
Keywords:
differential invariants, intelligent fault diagnosis, remaining useful life prediction, system architecture, industrial AI, robustness, data governance, fairness, sustainabilityAbstract
The increasing complexity of industrial systems and the proliferation of sensor data have driven a paradigm shift toward data-driven intelligent fault diagnosis and remaining useful life prediction. However, current deep learning-based approaches often suffer from brittle generalization when deployment conditions diverge from training distributions, undermining trust in safety-critical applications. This paper investigates the integration of differential invariant learning into industrial diagnostic and prognostic frameworks as a systemic strategy to address these vulnerabilities. Differential invariants, which capture intrinsic symmetries of physical processes, offer a principled route to extract features that remain stable across varying operating regimes, sensor configurations, and environmental conditions. We present a system-level analysis that situates data-driven invariant discovery within the broader architecture of industrial cyber-physical systems, examining structural trade-offs between model expressiveness, interpretability, and robustness. The discussion spans the entire lifecycle: from data governance and modular architectural design to deployment in edge-cloud hierarchies, continuous monitoring, and fairness-aware operation. We articulate how invariant representations can be embedded in federated learning pipelines to preserve privacy while enabling knowledge sharing across heterogeneous fleets. Furthermore, we explore the policy implications of deploying self-adaptive invariant-based systems, including certification, accountability, and equitable maintenance scheduling. By synthesizing insights from differential geometry, nonlinear dynamics, and socio-technical systems research, this paper provides a comprehensive roadmap for building trustworthy, sustainable, and resilient intelligent maintenance infrastructures. The analysis refrains from mathematical formalism, instead concentrating on conceptual foundations, structural design principles, and the governance frameworks necessary to transition invariant learning from laboratory demonstrations to operational industrial services.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
