Rotating machinery forms the critical backbone of infrastructure in a sustainable city, with bearings playing a pivotal role as key mechanical transmission components. Therefore, the health status of these bearings directly influences the safe operation of the infrastructure. Accurate and reliable diagnosis of defects in these components minimizes downtime, reduces maintenance costs, and prevents major accidents, ultimately providing insights in the construction and management of a sustainable city. Typically, in actual industrial scenarios, varying working conditions and various types of machines can result in significant discrepancies in the distribution of sample data. Moreover, the non-negligible noise may degrade the diagnostic performance. Therefore, realizing an accurate and reliable bearing diagnosis considering the cross-domain and noise environment remains a challenge. Leveraging the merits of information fusion and multi-source domain transfer learning, this article proposes a multi-source domain feature-decision dual fusion adversarial transfer network (DFATN) to break through the aforesaid limitations. Initially, an adversarial transfer framework is developed, incorporating novel feature matching evaluation and joint distribution difference losses. This framework is designed to facilitate the learning of feature invariants across domains and to enhance the sharing of domain-specific knowledge, even in noise. Relying on channel-spatial interactive feature fusion, a multi-scale feature extractor (MFE) is constructed to share the interaction and enhance the modeling of complex features in multiple dimensions. Additionally, a fault state-related decision fusion mechanism (SDF) is also implemented to integrate diagnostic information, significantly enhancing the generalization performance and robustness of the proposed network. By employing both public Paderborn University (PU) and laboratory-collected (Lab) datasets, the effectiveness and superiority of the proposed DFATN on bearing fault diagnosis are validated. For cross-working condition tasks, the proposed method realizes impressive performance, with average accuracies of 96.52% and 98.76% for Paderborn University (PU) and laboratory-collected (Lab) datasets, respectively. For cross-machine tasks, the average accuracy is 83.36%, outperforming other latest cross-domain fault diagnosis techniques.

中文翻译：

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一种用于可持续城市跨域抗噪声机械故障诊断的多源域特征判定双融合对抗转移网络


旋转机械是可持续城市基础设施的关键支柱，轴承作为关键的机械传动部件发挥着关键作用。因此，这些轴承的健康状况直接影响基础设施的安全运行。对这些组件中的缺陷进行准确可靠的诊断，最大限度地减少停机时间，降低维护成本，并防止重大事故，最终为可持续城市的建设和管理提供见解。通常，在实际工业场景中，不同的工作条件和各种类型的机器会导致样品数据的分布出现显著差异。此外，不可忽略的噪音可能会降低诊断性能。因此，考虑到跨域和噪声环境，实现准确可靠的轴承诊断仍然是一个挑战。利用信息融合和多源域迁移学习的优点，本文提出了一种多源域特征决策双融合对抗转移网络 （DFATN），以突破上述限制。最初，开发了一个对抗性转移框架，结合了新颖的特征匹配评估和联合分布差异损失。该框架旨在促进跨域特征不变量的学习，并增强特定域知识的共享，即使在噪声中也是如此。依托通道-空间交互特征融合，构建多尺度特征提取器 （MFE） 以共享交互并增强复杂特征的多维建模。 此外，还实现了一种与故障状态相关的决策融合机制 （SDF） 来集成诊断信息，显著提高了所提网络的泛化性能和鲁棒性。通过使用公共帕德博恩大学 （PU） 和实验室收集的 （Lab） 数据集，验证了所提出的 DFATN 在轴承故障诊断方面的有效性和优越性。对于交叉工作条件任务，所提方法实现了令人印象深刻的性能，帕德博恩大学 （PU） 和实验室收集 （Lab） 数据集的平均准确率分别为 96.52% 和 98.76%。对于跨机任务，平均准确率为 83.36%，优于其他最新的跨域故障诊断技术。