全断面硬岩隧道掘进机(TBM)变速箱在运行过程中长期承受强转矩作用，导致其磨损故障率较高，为准确识别出变速箱的磨损状态，避免更大故障的发生，提出一种基于改进卷积神经网络(CNN)的TBM变速箱磨损状态识别方法。首先，根据变速箱的磨损状态识别机制，选取光谱分析结果中9种元素的质量分数值作为变速箱磨损状态识别的指标； 然后，建立改进的卷积神经网络模型，使用2层卷积层堆叠代替传统模型中的1层卷积层深度提取数据特征，并使用2个尺寸不同的卷积核提取不同层级的特征； 最后，将该模型应用于实际工程中，将采集到的部分TBM变速箱油液光谱数据和磨损状态标签输入模型进行训练，使用训练好的模型对测试集数据进行磨损状态识别。研究结果表明，该方法能准确识别出TBM变速箱的磨损状态，准确率达到95%，可为实际施工中TBM变速箱的维护保养提供决策支持。

The gearbox of tunnel boring machines(TBMs) is subjected to continuous strong torque during operation, resulting in a high wear failure rate. Therefore, to accurately identify the wear state of the gearbox and avoid further faults, a convolutional neural network(CNN)based TBM gearbox wear state recognition method is proposed. First, based on the wear state identification mechanism of the gearbox, the concentration values of nine elements in the spectral analysis results are selected as indicators for identifying the wear state of the gearbox. Next, an improved CNN model is established. Two convolutional layers are stacked to replace the depth of one convolutional layer in the traditional model to extract data features, and two convolutional kernels with different sizes are used to extract features at different levels. Finally, the model is applied to a practical engineering project, and the collected TBM gearbox oil spectral data and wear state labels are put into the model for training. The trained model is used to identify the wear state of the test set data. By achieving 95% accuracy rate, the research results suggest that the proposed method can accurately identify the wear state of TBM gearboxes, providing decisionmaking support for maintenance in actual construction.