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The aim is to review recent RUL prediction methods and their applications in machine health estimation and fault diagnosis.

April 16, 2026Engineering ReportsOpen Access

Remaining Useful Life ( RUL ) Prediction Methods for Machine Health Estimation and Fault Diagnosis: A Comprehensive Review of Latest Techniques and Future Prospects

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The aim is to review recent RUL prediction methods and their applications in machine health estimation and fault diagnosis.
Conducted a comprehensive review of recent RUL prediction techniques including statistical, ML, and DL methods.
Discussed the strengths and limitations of model-based and data-driven approaches.
Highlighted the importance of AI learning models and hybrid methods in enhancing prediction accuracy.
Identified that model-based methods require comprehensive datasets and system knowledge to ensure accuracy.
Found that data-driven methods are increasingly popular for flexibility in handling complex degradation features.
Revealed that hybrid and ensemble methods can effectively combine strengths from both model-based and data-driven approaches.

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This review highlights advancements in RUL prediction methods, improving maintenance strategies across industries.

Amin et al. (2026) studied this question.

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