Broad Convolutional Neural Network Based Industrial Process Fault Diagnosis With Incremental Learning Capability
Broad Convolutional Neural Network Based Industrial Process Fault Diagnosis With Incremental Learning Capability
复制标题
具有增量学习能力的基于广泛卷积神经网络的工业过程故障诊断
DOI：
10.1109/tie.2019.2931255
复制
发表时间：
2020-06-01
影响因子：
7.7
通讯作者：
Zhao, Chunhui
中科院分区：
文献类型：
--
作者：
Yu, Wanke;Zhao, Chunhui
故障诊断，它确定了观察到的失控状态的根本原因，是必不可少的，以抵消或消除故障的工业过程。传统的数据驱动故障诊断方法忽略了异常样本的故障倾向性，需要一个完整的再训练过程来包含新采集的异常样本或故障类别。在本文中，设计了一个具有增量学习能力的广义卷积神经网络（BCNN）来解决上述问题。该方法将多个连续样本组合成一个数据矩阵，通过卷积运算从数据矩阵中提取故障趋势和非线性结构。之后，可以基于所获得的特征及其对应的故障标签来训练全连接层中的权重。由于该网络的架构，BCNN模型的诊断性能可以通过添加新生成的附加特征来提高。最后，还设计了所提出的方法的增量学习能力，使得BCNN模型可以更新自身以包括新的异常样本和故障类别。所提出的方法都适用于一个模拟过程和一个真实的工业过程。实验结果表明，该方法能更好地捕捉故障过程的特征，并有效地更新诊断模型，以包含新的异常样本和故障类别。
Fault diagnosis, which identifies the root cause of the observed out-of-control status, is essential to counteracting or eliminating faults in industrial processes. Many conventional data-driven fault diagnosis methods ignore the fault tendency of abnormal samples, and they need a complete retraining process to include the newly collected abnormal samples or fault classes. In this article, a broad convolutional neural network (BCNN) is designed with incremental learning capability for solving the aforementioned issues. The proposed method combines several consecutive samples as a data matrix, and it then extracts both fault tendency and nonlinear structure from the obtained data matrix by using convolutional operation. After that, the weights in fully connected layers can be trained based on the obtained features and their corresponding fault labels. Because of the architecture of this network, the diagnosis performance of the BCNN model can be improved by adding newly generated additional features. Finally, the incremental learning capability of the proposed method is also designed, so that the BCNN model can update itself to include new coming abnormal samples and fault classes. The proposed method is applied both to a simulated process and a real industrial process. Experimental results illustrate that it can better capture the characteristics of the fault process, and effectively update diagnosis model to include new coming abnormal samples, and fault classes.