Adaptive exploration and utilization of the correlations are the crucial factors in determining the performance of fusion based intelligent diagnosis methods. However, subject to the impact of harsh operating environments in industrial applications, collected multisource data are inevitably suffer from the challenge of incompleteness, directly put these correlations disabled incomplete multisource data for diagnosis necessarily leads to unsatisfactory results. Therefore, a novel wavelet-driven multisource fusion and reconstruction network (WFRN) is proposed, it innovatively adopts a physically interpretable fault knowledge transfer strategy to overcome the incompleteness challenges. Specifically, the developed missing feature reconstruction module is capable of transferring complete fault knowledge to the reconstruction of incomplete information, and the coordinated representation-based reconstruction enables the missing fault feature completion without the restriction of missing modes. Furthermore, the completed information is fused to generate more discriminative fault representations by integrating both views of multi-sensor and multi-time series. Finally, experimental results on an aviation rotor fault simulator not only validate the feasibility and superiority of the proposed WFRN, but also demonstrate its strong adaptability in addressing all potential missing modes in widespread industrial applications.

中文翻译：

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物理知识驱动的特征融合和重构网络，用于不完全多源数据的故障诊断


对相关性的适应性探索和利用是决定基于融合的智能诊断方法性能的关键因素。然而，受工业应用中恶劣作业环境的影响，采集的多源数据不可避免地受到不完备的挑战，直接把这些关联禁用的不完整多源数据进行诊断必然会导致不尽如人意的结果。因此，提出了一种新的小波驱动的多源融合与重建网络 （WFRN），它创新性地采用了物理可解释的故障知识传递策略来克服不完备性挑战。具体来说，开发的缺失特征重建模块能够将完整的故障知识转化为不完整信息的重建，基于协同表示的重建使得缺失故障特征的完成不受缺失模式的限制。此外，通过集成多传感器和多时间序列的视图，将完成的信息融合在一起，以生成更具鉴别性的故障表示。最后，在航空旋翼故障模拟器上的实验结果不仅验证了所提出的 WFRN 的可行性和优越性，而且证明了其在解决广泛工业应用中所有潜在缺失模式的强大适应性。