Machine Learning for Industrial Asset Failure Prediction: A Systematic Review of Adaptive, Context-Aware, and Explainable Frameworks

Abstract

Asset reliability is essential to operational excellence, safety and sustainability across asset-intensive industries where equipment failures can cause significant environmental, economic and regulatory impacts, such as oil and gas, manufacturing, utilities, power systems etc. Data-driven and machine learning (ML) based predictive maintenance (PdM) has progressed anomaly detection, fault diagnosis and remaining useful life (RUL) estimation, but most of the existing literature has been targeted at short-term predictive accuracy instead of long-term industrial deployability. This is a systematic literature review using the PRISMA approach and amalgamating 33 peer-reviewed papers published between 2020 and 2026. The studies which have been reviewed are categorized into six methodological groups: supervised learning, deep learning, hybrid and physics-informed models, environmental and context-aware systems, transfer learning and AutoML, and explainable and interpretable models. Despite the promising predictive results of the selected deep learning and hybrid models, such as R² values exceeding 0.99 and F1 scores above 0.93, significant deployment challenges still exist. Adaptive recalibration becomes a part of the explicit scope of the reviewed studies only in about 27% of the studies, while very few studies indicate adaptive recalibration as a function of the assets' age, operating-regime shift, or maintenance interventions. The review focuses on the area of industrial ML-based PdM, where the field moves from the algorithmic accuracy to context-aware, explainable, transferable and scalable asset-failure prediction.