Explainable AI for remaining useful life prediction in industrial systems: a survey

In Industry 4.0, the integration of artificial intelligence (AI) has transformed industrial operations, with predictive maintenance (PdM) emerging as a key application. As the core of PdM, remaining useful life (RUL) prediction enables a shift from reactive or preventive maintenance to proactive, data-driven strategies, helping to optimize scheduling, reduce downtime, and cut costs. Despite the high predictive accuracy of machine learning and deep learning models in RUL prediction, their inherent ‘black-box’ nature presents significant barriers to practical industrial adoption. These barriers include compromised trust in model outputs, difficulties in debugging and error tracing, challenges in meeting regulatory compliance requirements, and limited alignment with domain-specific expertise. Consequently, explainable AI (XAI) has become indispensable for addressing these issues and enabling wider deployment of RUL prediction systems in industrial settings. This paper presents a comprehensive survey of XAI for industrial RUL prediction. The survey begins by exploring the significance of RUL prediction, challenges of black-box models, the role of XAI, and its value in enhancing the reliability of RUL methods. Subsequently, it systematically synthesizes recent research advancements, categorizes XAI techniques into five types, and traces their technical evolution. It also develops a holistic evaluation framework encompassing approaches, properties, and metrics, supported by 11 functionally grounded quantitative indicators. Moreover, it establishes a comparative analysis system on eight typical public datasets to clarify their suitability for XAI integration and industrial applications, and puts forward future research directions that combine data, physics, large language models, and digital twins to bridge the gap between academic research and industrial deployment.

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