The early fault diagnosis is a kind of important technology to ensure the normal and reliable operation of wind turbines. However, due to the potential presence of noisy labels in health condition dataset and the weakly explanation of the deep neural network decisions, the performance of fault diagnosis is severely limited. In this paper, a framework called normalized recurrent neural network (NRNN) is proposed for noisy label fault diagnosis, in which the normalized long short-term memory is used to improve the training process and the forward crossentropy loss is introduced to handle the negative effect of noisy labels. The effectiveness and superiority of the proposed framework are verified by four datasets with different noisy label proportions. Meanwhile, the layer-wise relevance propagation algorithm is applied to explore the decision of framework and by visualizing the relevances of input samples to framework decisions, the NRNN does not treat samples equally and prefers signal peaks for classification decisions.

早期故障诊断是确保风机正常可靠运行的重要技术。但是，由于健康状况数据集中可能存在嘈杂标签，并且对深度神经网络决策的解释不力，因此故障诊断的性能受到严重限制。本文提出了一种用于噪声标签故障诊断的称为归一化递归神经网络（NRNN）的框架，其中使用归一化的长短期记忆来改善训练过程，并引入正向熵损失来处理负面影响。嘈杂的标签。所提出的框架的有效性和优越性由具有不同噪声标签比例的四个数据集验证。与此同时，