Automatic and accurate identification on the health condition of the diesel engine is a challenging task in the modern industry. In this paper, an innovative deep learning network structure called Random Convolutional Neural Network (RCNN) is designed for the intelligent health monitoring of diesel engines, taking full advantages of deep learning and ensemble learning. Firstly, this novel network framework is constructed with several individual convolutional neural networks (CNN), which can automatically extract the discriminative features of vibration signals by convolutional calculation and pooling operation. Secondly, an improved optimizer called Adabound and the Dropout technique are adopted in the framework of RCNN. The Adabound optimizer uses adaptive learning rates to accelerate the training of network and avoid plunging into local optimum. Finally, a combinational rule is used to fuse the diagnostic results from several individual CNN. The experimental vibration signals acquired from the diesel engine prove that the efficiency and superiority of the proposed RCNN.

在现代工业中，自动，准确地识别柴油发动机的健康状况是一项艰巨的任务。本文设计了一种创新的深度学习网络结构，称为随机卷积神经网络（RCNN），用于柴油机的智能健康监测，充分利用了深度学习和集成学习的优势。首先，这个新颖的网络框架由几个独立的卷积神经网络（CNN）构成，它们可以通过卷积计算和合并运算自动提取振动信号的判别特征。其次，在RCNN框架中采用了称为Adabound和Dropout技术的改进优化器。Adabound优化器使用自适应学习率来加速网络训练，并避免陷入局部最优状态。最后，使用组合规则融合来自多个单独CNN的诊断结果。从柴油机获得的实验振动信号证明了所提出的RCNN的效率和优越性。