Fault diagnosis of mechanical equipment provides robust support for industrial production. It is worth noting that, the operation of mechanical equipment is accompanied by changes in factors such as speed and load, leading to significant differences in data distribution, which pose challenges for fault diagnosis. Additionally, in terms of application deployment, commonly used cloud-based fault diagnosis methods often encounter issues such as time delays and data security concerns, while common fault diagnosis methods cannot be directly applied to edge computing devices. Therefore, conducting fault diagnosis under cross-operating conditions based on edge computing holds significant research value. This paper proposes a domain-adaptation-based lightweight fault diagnosis framework tailored for edge computing scenarios. Incorporating the local maximum mean discrepancy into knowledge transfer aligns the feature distributions of different domains in a high-dimensional feature space, to discover a common feature space across domains. The acquired fault diagnosis expertise from the cloud-based deep neural network model is transferred to the lightweight edge-based model (edge model) using adaptation knowledge transfer methods. It aims to achieve accurate fault diagnosis under cross-working conditions while ensuring real-time diagnosis capabilities. We utilized the NVIDIA Jetson Xavier NX kit as the edge computing platform and conducted validation experiments on two devices. In terms of diagnostic performance, the proposed method significantly improved diagnostic accuracy, with average increases of 34.44% and 17.33% compared to existing methods, respectively.

翻译：机械设备故障诊断为工业生产提供了有力支撑。值得注意的是，机械设备运行常伴随转速、负载等因素变化，导致数据分布差异显著，这给故障诊断带来了挑战。此外，在应用部署层面，常用的云端故障诊断方法常面临时延与数据安全问题，而常规故障诊断方法难以直接适用于边缘计算设备。因此，基于边缘计算开展跨工况故障诊断具有重要研究价值。本文提出一种面向边缘计算场景的基于域适应的轻量化故障诊断框架。通过将局部最大均值差异融入知识迁移过程，在高维特征空间中对齐不同域的特征分布，以发现跨域公共特征空间。利用自适应知识迁移方法，将云端深度神经网络模型习得的故障诊断知识迁移至轻量化边缘模型，旨在保证实时诊断能力的同时实现跨工况下的精准故障诊断。我们采用NVIDIA Jetson Xavier NX套件作为边缘计算平台，在两种设备上进行了验证实验。诊断性能方面，所提方法显著提升了诊断准确率，较现有方法平均分别提高了34.44%和17.33%。