A Review and Classification of Ageing Models of Ageing Models for District Heating Pipelines

Deterministic, Probabilistic, and Data-Driven Approaches

Abstract

District Heating (DH) systems have been an essential component of urban energy infrastructure for nearly a century, particularly in regions with severe winter climates, such as Northern Europe and the Baltic states. These networks reliably deliver thermal energy to residential, institutional, and industrial consumers. Like all infrastructure systems, the operational lifespan of DH pipelines is finite and is largely determined by material properties, operating conditions, and installation quality. To ensure long-term serviceability, investment planning, and effective asset management, it is critical to understand the ageing mechanisms of DH pipelines and to develop robust models for service life prediction and Remaining Useful Life (RUL) estimation. The European standard EN 253 provides quality assurance guidelines and minimum performance thresholds—typically specifying a 30-year minimum service life under defined operating conditions—for pre-insulated bonded pipe systems. While such standards offer valuable product-level benchmarks, they do not provide sufficient tools for predicting infrastructure-wide lifetime or RUL during operation. Existing literature classifies the ageing models as deterministic or probabilistic approaches, which they are respectively based on material science–based models, and on failure event statistics. These approaches are often described respectively as “Top-Down” (statistical) and “Bottom-Up” (material-based) methods. More recently, the emergence of machine learning and the increasing availability of operational and failure data have enabled the development of advanced data-driven models with improved predictive capabilities for both failure forecasting and remaining useful life estimation.

This paper presents a critical review and updated classification of ageing models for DH pipelines, organizing them into three main categories: deterministic, probabilistic, and data-driven approaches. The advantages and limitations of each class are discussed, with particular emphasis on their applicability to real-world DH network management and remaining useful life prediction. The proposed framework aims to support the development of integrated hybrid models that can better capture the complex, multifactorial ageing processes in DH systems and improve long-term infrastructure planning and maintenance strategies.