State-of-the-art artificial intelligence approaches for anomaly detection and remaining useful life prediction: a review

Background Accurate prediction of the remaining useful life (RUL) of assets is fundamental to the development of effective maintenance strategies and overall asset management. Despite significant advancements, there remains a notable gap in integrating fault detection and diagnostics (FDD) with RUL prediction models to create more comprehensive and accurate maintenance systems. One of the key challenges in this field is the limited ability of current models to generalize effectively across different types of equipment and varying operating conditions. This gap emphasizes the need for further research and innovation in developing robust and adaptable RUL prediction methodologies that can be applied broadly across diverse industrial scenarios. Methodology This review systematically evaluates the machine learning (ML) and deep learning (DL) techniques used for anomaly detection and RUL prediction, focusing on their efficacy and practical application. By adhering to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, the review identifies and addresses the deficiencies in existing models. It explores a range of machine learning and deep learning methods, including probabilistic approaches, hybrid models that combine multiple machine learning techniques, and neural networks designed to handle large-scale time-series data. The review also examines the potential for synergy between machine learning models and FDD, aiming to enhance the precision of equipment monitoring and the early detection of defects. The challenges of data variability, the irregularity in equipment deterioration, and the interpretability of complex models are highlighted. Results The analysis reveals that while current machine learning and deep learning models have made considerable strides in predicting the RUL of assets, significant challenges remain, particularly in their ability to generalize across various equipment types and operational contexts. Hybrid models and neural networks have shown promise in improving the accuracy of RUL predictions, especially when managing large, complex datasets. However, the irregular nature of equipment wears and tear, coupled with data variability, continues to pose significant challenges. The review highlights the need for more robust and adaptable models that can not only predict RUL more accurately but also integrate seamlessly with FDD systems to provide a more holistic approach to maintenance. Conclusion This comprehensive review focusses on the need for continued research in developing more integrated, generalizable, and efficient predictive maintenance systems. By exploring the application of AI in virtual assistants, the review suggests promising avenues for extending asset longevity and optimizing maintenance schedules. While current models offer valuable insights, they must evolve to address the identified gaps in generalizability and model interpretability.