Collaborative fault diagnosis can be facilitated by multisensory fusion technologies, as these can give more reliable results with a more complete data set. Although deep learning approaches have been developed to overcome the problem of relying on subjective experience in conventional fault diagnosis, there are two remaining obstacles to collaborative efficiency: integration of multisensory data and fusion of maintenance strategies. To overcome these obstacles, we propose a novel two-part approach: a stacked wavelet auto-encoder structure with a Morlet wavelet function for multisensory data fusion and a flexible weighted assignment of fusion strategies. Taking a planetary gearbox as an example, we use noisy vibration signals from multisensors to test the diagnosis performance of the proposed approach. The results demonstrate that it can provide more accurate and reliable fault diagnosis results than other approaches.

多传感器融合技术可以促进协作式故障诊断，因为这些技术可以通过更完整的数据集提供更可靠的结果。尽管已经开发了深度学习方法来克服传统故障诊断中依赖主观经验的问题，但协作效率仍然存在两个障碍：多传感器数据的集成和维护策略的融合。为了克服这些障碍，我们提出了一种新颖的两部分方法：具有Morlet小波函数的堆叠小波自动编码器结构，用于多传感器数据融合以及融合策略的灵活加权分配。以行星齿轮箱为例，我们使用来自多传感器的噪声振动信号来测试所提出方法的诊断性能。