Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Korea.
School of Computer Science and Engineering, Soongsil University, Seoul 06978, Korea.
Sensors (Basel). 2021 Oct 4;21(19):6614. doi: 10.3390/s21196614.
Deep learning (DL) plays a very important role in the fault diagnosis of rotating machinery. To enhance the self-learning capacity and improve the intelligent diagnosis accuracy of DL for rotating machinery, a novel hybrid deep learning method (NHDLM) based on Extended Deep Convolutional Neural Networks with Wide First-layer Kernels (EWDCNN) and long short-term memory (LSTM) is proposed for complex environments. First, the EWDCNN method is presented by extending the convolution layer of WDCNN, which can further improve automatic feature extraction. The LSTM then changes the geometric architecture of the EWDCNN to produce a novel hybrid method (NHDLM), which further improves the performance for feature classification. Compared with CNN, WDCNN, and EWDCNN, the proposed NHDLM method has the greatest performance and identification accuracy for the fault diagnosis of rotating machinery.
深度学习（DL）在旋转机械故障诊断中起着非常重要的作用。为了提高深度学习对旋转机械的自学习能力和智能诊断精度，针对复杂环境，提出了一种基于扩展宽卷积核深度卷积神经网络（EWDCNN）和长短时记忆网络（LSTM）的新型混合深度学习方法（NHDLM）。首先，通过扩展 WDCNN 的卷积层提出 EWDCNN 方法，从而可以进一步提高自动特征提取能力。然后，LSTM 改变 EWDCNN 的几何结构，产生一种新的混合方法（NHDLM），从而进一步提高特征分类的性能。与 CNN、WDCNN 和 EWDCNN 相比，所提出的 NHDLM 方法在旋转机械故障诊断中的性能和识别准确率最高。
