A Multi-Criteria Framework for Selecting Machine Learning Techniques for Industrial Fault Prognosis

Abstract

Selecting appropriate Machine Learning (ML) techniques for fault prognosis remains a critical yet often understructured step in developing predictive maintenance strategies for industrial systems. While numerous studies apply ML to estimate Remaining Useful Life (RUL) or forecast failure probabilities, model selection is frequently guided by ad hoc practices or narrow performance metrics, overlooking contextual factors such as data availability, interpretability, automation level, and deployment feasibility. This paper presents a parameterized, multi-criteria decision-making framework to support ML techniques selection in fault prognosis, particularly within energy-related applications. Derived from a structured literature survey, the framework introduces twelve selection parameters, divided into primary requirements and secondary evaluation criteria. These parameters, such as label requirements, model complexity, and transferability, allow users to eliminate unsuitable techniques and rank viable candidates according to application-specific constraints. The framework is applied to a real-world use case involving failure prediction in electrical substations, illustrating how it supports transparent, replicable, and operationally grounded model selection. Results demonstrate the framework’s adaptability to different industrial contexts and its relevance for decision-making in energy systems. By bridging empirical insights with implementation demands, the proposed approach offers a practical tool for aligning ML technique selection with the goals of energy-sector prognostics and maintenance planning.