Li Zhibo, Li Yuanyuan, Sun Qichun, Qi Bowei
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
Entropy (Basel). 2022 Nov 2;24(11):1589. doi: 10.3390/e24111589.
An effective fault diagnosis method of bearing is the key to predictive maintenance of modern industrial equipment. With the single use of equipment failure mechanism or operation of data, it is hard to resolve multiple complex variable working conditions, multiple types of fault and equipment malfunctions and failures related to knowledge and data. In order to solve these problems, a fault diagnosis method based on the fusion of deep learning with a knowledge graph is proposed in this paper. Firstly, the knowledge rules of bearing data is used for entity extraction. Next, the multiscale optimized convolutional neural network (MOCNN) proposed in this paper is used for fault classification to achieve relationship extraction. Finally, the fault diagnosis graph of the bearing is constructed for fault-assisted decision-making as well as the detailed display of fault information. According to experiment analysis, the fault diagnosis model based on MOCNN proposed in this paper, which integrates the end-to-end convolutional neural network and the attention mechanism, still achieves an accuracy of 97.86% under the data set of 160 types of faults. Compared with the deep learning models such as Resnet and Inception in the noise environment of multiple working conditions and variable working conditions, the model proposed in this paper not only shows a faster convergence speed and stable performance, but also a higher accuracy in evaluation indicators, which is beneficial to practical use.
一种有效的轴承故障诊断方法是现代工业设备预测性维护的关键。仅依靠设备故障机理或运行数据，难以解决多复杂变量工况、多种故障类型以及与知识和数据相关的设备故障与失效问题。为解决这些问题，本文提出一种基于深度学习与知识图谱融合的故障诊断方法。首先，利用轴承数据的知识规则进行实体提取。其次，使用本文提出的多尺度优化卷积神经网络（MOCNN）进行故障分类以实现关系提取。最后，构建轴承故障诊断图用于故障辅助决策以及故障信息的详细展示。通过实验分析，本文提出的基于MOCNN的故障诊断模型，融合了端到端卷积神经网络和注意力机制，在160种故障类型的数据集下仍达到了97.86%的准确率。在多工况和变工况噪声环境下，与Resnet和Inception等深度学习模型相比，本文提出的模型不仅收敛速度更快、性能更稳定，而且在评估指标上具有更高的准确率，有利于实际应用。
