基于深度学习的道岔转辙机故障诊断技术研究综述

A Review of Turnout Switch Machine Fault Diagnosis Technology Based on Deep Learning

[目的]对基于深度学习的道岔转辙机故障诊断技术进行归纳总结,分析各种深度学习方法在转辙机故障诊断中的应用现状,探讨其优势与局限性,并提出未来研究方向。[方法]首先介绍了转辙机故障诊断的重要性及所面临的挑战,随后对比分析了模型驱动和数据驱动两种诊断方法的特点。接着详细阐述了基于深度神经网络、自动编码器、卷积神经网络、循环神经网络以及多深度模型混合的故障诊断方法,对比了不同方法的特点。最后讨论了当前研究的局限性,包括对大量标注数据的需求、模型复杂度与解释性等问题,并提出了几点未来研究方向。[结果及结论]基于深度学习的转辙机故障诊断技术展现出了强大的特征提取和数据处理能力,有效提升了故障诊断的准确性和效率。然而,现有的深度学习仍面临数据需求量大、模型复杂度高和解释性差等挑战。未来深度学习的研究应注重数据预处理技术、多源信息融合、不平衡小样本场景下的诊断方法、迁移故障诊断以及可解释性深度诊断模型等方面,以推动深度学习在转辙机故障诊断领域的广泛应用和智能化水平的提升。

[Objective]The turnout switch machine fault diagnosis techniques based on deep learning is summarized,the application status of various deep learning methods in turnout switch machine fault diagnosis is analyzed,their advantages and limitations are explored,and future research directions are proposed.[Method]The importance and challenges of turnout switch machine fault diagnosis are first introduced,followed by a comparative analysis of the characteristics of model-driven and data-driven diagnostic approaches.Then,fault diagnosis methods based on deep neural networks,autoencoders,convolutional neural networks,recurrent neural networks,and hybrid multi-deep models are elaborated in-depth,with comparisons of their respective performance characteristics.The limitations of current research,including the need for large amounts of labeled data,model complexity,and interpretability are discussed.Several future research directions are proposed.[Result&Conclusion]Turnout switch machine fault diagnosis techniques based on deep learning demonstrate strong capabilities in feature extraction and data processing,significantly improving diagnostic accuracy and efficiency.However,current deep learning methods face challenges such as the requirement for large datasets,high model complexity,and limited interpretability.Future research should focus on data preprocessing techniques,multi-source information fusion,diagnosis methods for imbalanced and small sample scenarios,transfer fault diagnosis,and interpretable deep diagnostic models to enhance the wide application and intelligence level of deep learning in turnout switch machine fault diagnosis.