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Intelligent fault recognition framework by using deep reinforcement learning with one dimension convolution and improved actor-critic algorithm
Advanced Engineering Informatics ( IF 8.8 ) Pub Date : 2021-06-29 , DOI: 10.1016/j.aei.2021.101315
Zisheng Wang , Jianping Xuan

The quality of fault recognition part is one of the key factors affecting the efficiency of intelligent manufacturing. Many excellent achievements in deep learning (DL) have been realized recently as methods of fault recognition. However, DL models have inherent shortcomings. In particular, the phenomenon of over-fitting or degradation suggests that such an intelligent algorithm cannot fully use its feature perception ability. Researchers have mainly adapted the network architecture for fault diagnosis, but the above limitations are not taken into account. In this study, we propose a novel deep reinforcement learning method that combines the perception of DL with the decision-making ability of reinforcement learning. This method enhances the classification accuracy of the DL module to autonomously learn much more knowledge hidden in raw data. The proposed method based on the convolutional neural network (CNN) also adopts an improved actor-critic algorithm for fault recognition. The important parts in standard actor-critic algorithm, such as environment, neural network, reward, and loss functions, have been fully considered in improved actor-critic algorithm. Additionally, to fully distinguish compound faults under heavy background noise, multi-channel signals are first stacked synchronously and then input into the model in the end-to-end training mode. The diagnostic results on the compound fault of the bearing and tool in the machine tool experimental system show that compared with other methods, the proposed network structure has more accurate results. These findings demonstrate that under the guidance of the improved actor-critic algorithm and processing method for multi-channel data, the proposed method thus has stronger exploration performance.



中文翻译:

通过使用具有一维卷积的深度强化学习和改进的actor-critic算法的智能故障识别框架

故障识别部分的质量是影响智能制造效率的关键因素之一。最近,深度学习 (DL) 中的许多卓越成就已作为故障识别方法得到实现。但是,DL 模型具有固有的缺点。尤其是过拟合或退化的现象表明这种智能算法不能充分发挥其特征感知能力。研究人员主要采用了网络架构进行故障诊断,但没有考虑到上述局限性。在这项研究中,我们提出了一种新颖的深度强化学习方法,它将深度学习的感知与强化学习的决策能力相结合。这种方法提高了 DL 模块的分类精度,可以自主学习更多隐藏在原始数据中的知识。所提出的基于卷积神经网络(CNN)的方法还采用了改进的actor-critic算法进行故障识别。改进的actor-critic算法充分考虑了标准actor-critic算法中的重要部分,如环境、神经网络、奖励和损失函数。此外,为了充分区分大背景噪声下的复合故障,多通道信号首先同步堆叠,然后以端到端的训练模式输入模型。机床实验系统对轴承与刀具复合故障的诊断结果表明,与其他方法相比,所提出的网络结构具有更准确的结果。

更新日期:2021-06-29
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