Understanding Deterioration Random Effects for Causal Discovery in Infrastructure Management
arXiv:2605.20400v1 Announce Type: cross Abstract: Infrastructure deterioration poses significant challenges for asset management, yet existing approaches rely on population-averaged models that overlook equipment-specific heterogeneity. We present a novel framework that combines Bayesian hierarchical hazard modeling with causal discovery to identify operational patterns that drive heterogeneous deterioration rates in pump equipment. Our approach first estimates pump-specific random effects $u_i$ using GPU-accelerated No-U-Turn Sampling (NUTS), achieving 3--5$\times$ speedup over CPU implementa
The increasing availability of computational power and advanced sampling techniques like NUTS is enabling more sophisticated causal discovery methods for complex systems.
This development allows for more precise and equipment-specific infrastructure management, moving beyond generalized models to address heterogeneous deterioration rates more effectively.
Infrastructure maintenance strategies can shift from population-averaged assumptions to data-driven, nuanced interventions tailored to individual equipment, potentially extending asset lifespan and reducing costs.
- · Infrastructure asset managers
- · Predictive maintenance software providers
- · GPU manufacturers
- · Causal AI developers
- · Traditional infrastructure management consultancies relying on averaged models
- · Organizations with legacy, non-digitized infrastructure assets
Improved efficiency and longevity of critical infrastructure through data-driven maintenance.
Development of specialized AI tools for various industrial asset management domains, beyond just pumps.
Enhanced resilience of national infrastructure against operational failures, impacting economic stability and public safety.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG