Institute of Vibration and Noise, Naval University of Engineering, Wuhan 430033, China.
Naval Key Laboratory of Ship Vibration and Noise, Naval University of Engineering, Wuhan 430033, China.
Sensors (Basel). 2022 Aug 29;22(17):6489. doi: 10.3390/s22176489.
Mechanical equipment failure may cause massive economic and even life loss. Therefore, the diagnosis of the failures of machine parts in time is crucial. The rolling bearings are one of the most valuable parts, which have attracted the focus of fault diagnosis. Many successful rolling bearing fault diagnoses have been made based on machine learning and deep learning. However, most diagnosis methods still rely on complex signal processing and artificial features, bringing many costs to the deployment and migration of diagnostic models. This paper proposes an end-to-end continuous/discontinuous feature fusion method for rolling bearing fault diagnosis (C/D-FUSA). This method comprises long short-term memory (LSTM), convolutional neural networks (CNN) and attention mechanism, which automatically extracts the continuous and discontinuous features from vibration signals for fault diagnosis. We also propose a contextual-dependent attention module for the LSTM layers. We compare the method with the other simpler deep learning methods and state-of-the-art methods in rolling bearing fault data sets with different sample rates. The results show that our method is more accurate than the other methods with real-time inference. It is also easy to be deployed and trained in a new environment.
机械设备故障可能会导致巨大的经济损失，甚至生命损失。因此，及时诊断机器部件的故障至关重要。滚动轴承是最有价值的部件之一，已引起故障诊断的关注。许多成功的滚动轴承故障诊断都是基于机器学习和深度学习的。然而，大多数诊断方法仍然依赖于复杂的信号处理和人工特征，给诊断模型的部署和迁移带来了许多成本。本文提出了一种用于滚动轴承故障诊断的端到端连续/不连续特征融合方法（C/D-FUSA）。该方法包括长短时记忆（LSTM）、卷积神经网络（CNN）和注意力机制，可自动从振动信号中提取连续和不连续特征以进行故障诊断。我们还为 LSTM 层提出了一种上下文相关的注意力模块。我们在不同采样率的滚动轴承故障数据集上，将该方法与其他更简单的深度学习方法和最先进的方法进行了比较。结果表明，与其他方法相比，我们的方法具有更高的准确性和实时推断能力，并且易于在新环境中部署和训练。
