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ART-KOHONEN neural network for fault diagnosis of rotating machinery

机译：ART-KOHONEN神经网络用于旋转机械故障诊断

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摘要

In this paper, a new neural network (NN) for fault diagnosis of rotating machinery which synthesises the theory of adaptive resonance theory (ART) and the learning strategy of Kohonen neural network (KNN), is proposed. For NNs, as the new case occurs, the corresponding data should be added to their dataset for learning. However, the 'off-line' NNs are unable to adapt autonomously and must be retrained by applying the complete dataset including the new data. The ART networks can solve the plasticity-stability dilemma. In other words, they are able to carry out 'on-line' training without forgetting previously trained patterns (stable training); it can recede previously trained categories adaptive to changes in the environment and is self-organising. ART-KNN also holds these characteristics, and more suitable than original ART for fault diagnosis of machinery. In order to test the proposed network, the vibration signal is selected as raw inputs due to its simplicity, accuracy and efficiency. The results of the experiments confirm the performance of the proposed network through comparing with other NNs, such as the self-organising feature maps (SOFMs), learning vector quantisation (LVQ) and radial basis function (RBF) NNs under the same conditions. The diagnosis success rate for the ART-Kohonen network was 100%, while the rates of SOFM, LVQ and RBF networks were 93%, 93% and 89%, respectively.
机译：本文提出了一种新的用于旋转机械故障诊断的神经网络（NN），该算法综合了自适应共振理论（ART）的理论和Kohonen神经网络（KNN）的学习策略。对于神经网络，随着新情况的发生，应将相应的数据添加到其数据集中进行学习。但是，“离线” NN不能自主适应，必须通过应用包括新数据的完整数据集进行重新训练。 ART网络可以解决可塑性-稳定性难题。换句话说，他们能够进行“在线”培训而不会忘记以前的培训模式（稳定培训）；它可以使以前受过培训的类别适应环境的变化，并且是自组织的。 ART-KNN也具有这些特征，并且比原始ART更适合用于机械故障诊断。为了测试所提出的网络，振动信号由于其简单性，准确性和效率而被选择为原始输入。实验结果通过与其他NN（例如自组织特征图（SOFM），学习矢量量化（LVQ）和径向基函数（RBF）NN）在相同条件下的比较，证实了所提出网络的性能。 ART-Kohonen网络的诊断成功率为100％，SOFM，LVQ和RBF网络的诊断成功率分别为93％，93％和89％。

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