迁移学习驱动机械装备智能故障诊断方法综述

Transfer learning-driven intelligent fault diagnosis for mechanical equipment methods review

关键词:  机械装备  智能故障诊断  迁移学习  故障预测与健康管理（PHM）  工业大数据

Key words:  mechanical equipment  intelligent fault diagnosis  transfer learning  prognostics and health management (PHM)  industrial big data

摘要:

随着工业传感器的广泛部署和人工智能算法的快速发展,基于数据驱动的智能故障诊断技术已成为机械装备故障预测与健康管理（PHM）的关键部分和热点话题,然而,此类方法依赖于大量标记数据且对数据分布具有严格的一致性要求,导致相关方法在真实工业场景中的准确性和鲁棒性大幅下降。迁移学习作为应对数据分布不一致与小样本故障诊断问题的有效手段,得到了学术界与工业界的广泛关注,其通过将源域中学习到的知识迁移到目标域,显著提升了模型在目标域的泛化性能。为研究基于迁移学习的机械装备智能故障诊断方法的发展现状及其亟需解决的关键技术难题,对目前该领域的文献进行了分析与总结。首先,系统性梳理了机械装备智能故障诊断领域的国内、外研究进展与现状。其次,围绕迁移学习技术,分析对比各类迁移学习故障诊断方法的优势与局限性,从不同应用场景与行业关键技术问题出发,对迁移学习驱动的机械装备智能故障诊断技术进行了总结与评述。最后,探讨了相关热点问题并对技术瓶颈进行深入分析,指出了应对现有挑战的可能途径和未来发展趋势。 研究表明:迁移学习在机械装备智能故障诊断领域已引发广泛关注,但仍存在诸多技术难题亟需解决,随着人工智能技术的快速发展及本领域专家学者对迁移学习理论与应用研究的持续推进,可为机械装备智能故障诊断方法的开发提供坚实的理论与技术基础。

Abstract:

With the extensive deployment of industrial sensors and the rapid development of artificial intelligence algorithms, data-driven intelligent fault diagnosis technology has become a key part and a hot topic in Prognostics and Health Management of mechanical equipment. However, such methods rely on substantial labeled data and demand strict consistency in data distribution, thereby causing a significant drop in the accuracy and robustness of related methods in real industrial scenarios. Transfer learning, as an effective approach to tackling the problems of inconsistent data distribution and small-sample fault diagnosis, has drawn widespread attention from both academia and industry. It markedly enhances the generalization performance of the model in the target domain by transferring the knowledge acquired in the source domain to the target domain. To investigate the current state of transfer learning-driven intelligent fault diagnosis methods for mechanical equipment and the crucial technical challenges that urgently need to be resolved, an analysis and summary of the existing literature in this field have been carried out. Firstly, the research progress and current status of intelligent fault diagnosis for mechanical equipment in domestic and international studies have been systematically reviewed. Then, focusing on transfer learning technologies, the advantages and limitations of various transfer learning fault diagnosis methods have been analyzed and compared. From the perspectives of different application scenarios and key technical issues in the industry, the intelligent fault diagnosis technologies for mechanical equipment driven by transfer learning have been summarized and critically evaluated. Finally, current research hotspots have been explored and the technical bottlenecks have been thoroughly analyzed, and potential solutions to existing challenges along with future development trends are identified. Studies show that while transfer learning has garnered widespread attention in the field of intelligent fault diagnosis for mechanical equipment, many technical issues remain to be resolved. With the rapid development of artificial intelligence technologies and the continued efforts of experts and scholars in advancing transfer learning theories and applications, a solid theoretical and technical foundation can be established for the development of intelligent fault diagnosis methods for mechanical equipment.