系统梳理卷积神经网络在机械故障诊断中的应用与挑战
Advancing machine fault diagnosis: A detailed examination of convolutional neural networks
- 从理论到实践，全面分析CNN在故障检测中的架构与实现
- 揭示CNN在复杂数据和多环境下的诊断有效性与局限性
- 适合关注智能运维与工业AI的工程师和研究者阅读
随着机械设备日益复杂，对运行效率与安全性的需求推动了先进故障诊断技术的发展。在诸多方法中，卷积神经网络（CNN）因其强大的故障检测与分类能力脱颖而出。本文系统综述了CNN在机械故障诊断中的应用，涵盖其理论基础、架构演变及实际部署。深入分析了CNN在处理不同类型故障、复杂数据及多样工况时的优劣表现。同时探讨了数据增强、迁移学习与混合架构等近期进展。最后指出了未来研究方向与潜在挑战，旨在进一步提升CNN在可靠、主动式故障诊断中的应用水平。
原文摘要 · Abstract (English)
The growing complexity of machinery and the increasing demand for operational efficiency and safety have driven the development of advanced fault diagnosis techniques. Among these, convolutional neural networks (CNNs) have emerged as a powerful tool, offering robust and accurate fault detection and classification capabilities. This comprehensive review delves into the application of CNNs in machine fault diagnosis, covering its theoretical foundation, architectural variations, and practical implementations. The strengths and limitations of CNNs are analyzed in this domain, discussing their effectiveness in handling various fault types, data complexities, and operational environments. Furthermore, we explore the evolving landscape of CNN-based fault diagnosis, examining recent advancements in data augmentation, transfer learning, and hybrid architectures. Finally, we highlight future research directions and potential challenges to further enhance the application of CNNs for reliable and proactive machine fault diagnosis.
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