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首页> 外文期刊>IEEE transactions on industrial informatics >Understanding and Learning Discriminant Features based on Multiattention 1DCNN for Wheelset Bearing Fault Diagnosis
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Understanding and Learning Discriminant Features based on Multiattention 1DCNN for Wheelset Bearing Fault Diagnosis

机译：基于多目标1dcnn的Wheelset轴承故障诊断的理解和学习判别特征

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Recently, deep-learning-based fault diagnosis methods have been widely studied for rolling bearings. However, these neural networks are lack of interpretability for fault diagnosis tasks. That is, how to understand and learn discriminant fault features from complex monitoring signals remains a great challenge. Considering this challenge, this article explores the use of the attention mechanism in fault diagnosis networks and designs attention module by fully considering characteristics of rolling bearing faults to enhance fault-related features and to ignore irrelevant features. Powered by the proposed attention mechanism, a multiattention one-dimensional convolutional neural network (MA1DCNN) is further proposed to diagnose wheelset bearing faults. The MA1DCNN can adaptively recalibrate features of each layer and can enhance the feature learning of fault impulses. Experimental results on the wheelset bearing dataset show that the proposed multiattention mechanism can significantly improve the discriminant feature representation, thus the MA1DCNN outperforms eight state-of-the-arts networks.
机译：最近，基于深度学习的故障诊断方法已经广泛研究了滚动轴承。然而，这些神经网络缺乏对故障诊断任务的可解释性。也就是说，如何理解和学习复杂的监控信号的判别故障特征仍然是一个很大的挑战。考虑到这一挑战，本文通过充分考虑滚动轴承故障的特性来提高故障诊断网络中的注意机制，并设计注意力模块，以增强相关的特征，并忽略无关的功能。由所提出的注意机构提供动力，进一步提出了一种多节的一维卷积神经网络（MA1DCNN）以诊断轮圈轴承故障。 MA1DCNN可以自适应地重新校准每层的特征，并且可以增强故障冲动的特征学习。轴承数据集上的实验结果表明，所提出的多周机制可以显着改善判别特征表示，因此MA1DCNN优于八个最先进的网络。

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