Automatic and reliable fault diagnosis of rotating machinery cross working conditions is of practical importance. For this purpose, ensemble transfer convolutional neural networks (CNNs) driven by multi-channel signals are proposed in this paper. Firstly, a series of source CNNs modified with stochastic pooling and Leaky rectified linear unit (LReLU) are pre-trained using multi-channel signals. Secondly, the learned parameter knowledge of each individual source CNN is transferred to initialize the corresponding target CNN which is then fine-tuned by a few target training samples. Finally, a new decision fusion strategy is designed to flexibly fuse each individual target CNN to obtain the comprehensive result. The proposed method is used to analyze multi-channel signals measured from rotating machinery. The comparison result shows the superiorities of the proposed method over the existing deep transfer learning methods.

旋转机械交叉工作条件下的自动，可靠的故障诊断具有实际意义。为此，本文提出了由多通道信号驱动的集成转移卷积神经网络（CNN）。首先，使用多通道信号对经过随机池和泄漏整流线性单元（LReLU）修改的一系列源CNN进行预训练。其次，传输每个单个源CNN的学习到的参数知识，以初始化相应的目标CNN，然后通过几个目标训练样本对其进行微调。最后，设计了一种新的决策融合策略，可以灵活地融合每个目标CNN，以获得综合结果。所提出的方法用于分析从旋转机械测量的多通道信号。