Leite Denis, Andrade Emmanuel, Rativa Diego, Maciel Alexandre M A
Mekatronik I.C. Automacao Ltda, Rua Sargento Silvino Macedo, 130-Imbiribeira, Recife 51160-060, PE, Brazil.
Instituto de Inovação Tecnológica-IIT, Universidade de Pernambuco-UPE R. Min. Mario Andreaza, s/n-Várzea, Recife 50950-050, PE, Brazil.
Sensors (Basel). 2024 Dec 25;25(1):60. doi: 10.3390/s25010060.
Integrating Machine Learning (ML) in industrial settings has become a cornerstone of Industry 4.0, aiming to enhance production system reliability and efficiency through Real-Time Fault Detection and Diagnosis (RT-FDD). This paper conducts a comprehensive literature review of ML-based RT-FDD. Out of 805 documents, 29 studies were identified as noteworthy for presenting innovative methods that address the complexities and challenges associated with fault detection. While ML-based RT-FDD offers different benefits, including fault prediction accuracy, it faces challenges in data quality, model interpretability, and integration complexities. This review identifies a gap in industrial implementation outcomes that opens new research opportunities. Future Fault Detection and Diagnosis (FDD) research may prioritize standardized datasets to ensure reproducibility and facilitate comparative evaluations. Furthermore, there is a pressing need to refine techniques for handling unbalanced datasets and improving feature extraction for temporal series data. Implementing Explainable Artificial Intelligence (AI) (XAI) tailored to industrial fault detection is imperative for enhancing interpretability and trustworthiness. Subsequent studies must emphasize comprehensive comparative evaluations, reducing reliance on specialized expertise, documenting real-world outcomes, addressing data challenges, and bolstering real-time capabilities and integration. By addressing these avenues, the field can propel the advancement of ML-based RT-FDD methodologies, ensuring their effectiveness and relevance in industrial contexts.
在工业环境中集成机器学习（ML）已成为工业4.0的基石，旨在通过实时故障检测与诊断（RT-FDD）提高生产系统的可靠性和效率。本文对基于ML的RT-FDD进行了全面的文献综述。在805篇文献中，有29项研究因提出解决与故障检测相关的复杂性和挑战的创新方法而被认为值得关注。虽然基于ML的RT-FDD有不同的优势，包括故障预测准确性，但它在数据质量、模型可解释性和集成复杂性方面面临挑战。本综述发现工业实施成果方面存在差距，这为新的研究机会打开了大门。未来的故障检测与诊断（FDD）研究可能会优先考虑标准化数据集，以确保可重复性并便于进行比较评估。此外，迫切需要改进处理不平衡数据集的技术，并改善时间序列数据的特征提取。实施针对工业故障检测的可解释人工智能（AI）（XAI）对于提高可解释性和可信度至关重要。后续研究必须强调全面的比较评估，减少对专业知识的依赖，记录实际结果，解决数据挑战，并增强实时能力和集成。通过解决这些途径，该领域可以推动基于ML的RT-FDD方法的进步，确保它们在工业环境中的有效性和相关性。
