Fault diagnosis of rotating machinery plays a important role for the safety and stability of modern industrial systems. However, there is a distribution discrepancy between training data and data of real-world operation scenarios, which causing the decrease of performance of existing systems. This paper proposed a transfer learning based method utilizing acoustic and vibration signal to address this distribution discrepancy. We designed the acoustic and vibration feature fusion MAVgram to offer richer and more reliable information of faults, coordinating with a DNN-based classifier to obtain more effective diagnosis representation. The backbone was pre-trained and then fine-tuned to obtained excellent performance of the target task. Experimental results demonstrate the effectiveness of the proposed method, and achieved improved performance compared to STgram-MFN.

翻译：旋转机械的故障诊断对现代工业系统的安全与稳定性至关重要。然而，训练数据与实际运行场景数据之间存在分布差异，导致现有系统性能下降。本文提出一种基于迁移学习的方法，利用声学与振动信号解决这一分布差异问题。我们设计了声学与振动特征融合的MAVgram，以提供更丰富、可靠的故障信息，并结合基于深度神经网络的分类器，获得更有效的诊断表征。骨干网络经过预训练后通过微调，在目标任务中取得了优异性能。实验结果表明，该方法具有有效性，与STgram-MFN相比实现了性能提升。