Abstract: This paper proposes a fault diagnosis method based on regularized threshold wavelet decomposition, Markov transition field (MTF), and SE-DenseNet model, to address the problem of low diagnostic accuracy of ship power gearboxes caused by variable noise and complex working conditions in their operating environment. First, the squeeze-and-excitation networks (SENet) module is integrated into densely connected convolutional networks (DenseNet) to improve the model's feature extraction capability for MTF images. Regularized threshold wavelet decomposition is then used to denoise the original data. The preprocessed one-dimensional time-series data is transformed into a two-dimensional MTF image, which is fed into the improved DenseNet model for fault diagnosis, with a classification output head achieving accurate identification of fault types. The model is experimentally validated using the gear fault dataset from Southeast University and the fault data collected by a self-built ship power gearbox fault analysis unit. On the Southeast University dataset, the accuracy reaches 98.8% and 96.6% under two different working conditions, respectively. On the self-built dataset, the accuracy reaches 97.43% and 98.14% under two working conditions, respectively. The results show that the proposed model can identify fault types with high accuracy. Compared with other algorithm models, it exhibits good accuracy and generalization under different working conditions, providing a reference for the fault diagnosis of ship power gearboxes.

Key words: fault diagnosis, regularized threshold wavelet denoising, Markov transition field (MTF), densely connected convolutional networks (DenseNet), squeeze-and-excitation networks (SENet) module

摘要： 针对船舶动力齿轮箱工作环境中噪声多变、工况复杂等原因所导致的故障诊断准确率低的问题,该文提出了一种基于正则化阈值小波降噪、马尔可夫转移场（Markov transition field,MTF）与SE-DenseNet 模型的故障诊断方法。首先,在密集连接神经网络（densely connected convolutional networks,DenseNet）中结合压缩-激励网络（squeeze-and-excitation networks,SENet）模块,用以提高模型对MTF图的特征提取能力;再使用正则化阈值小波分解对原始数据进行降噪预处理,将预处理后的一维时序数据转化为二维MTF,输入到改进DenseNet模型中进行故障诊断,通过分类输出层实现对故障类型的精准诊断;利用东南大学齿轮故障数据集与自建船舶动力齿轮箱故障分析单元采集的故障数据,对模型进行试验验证,发现在东南大学数据集两种不同工况下分别达到了98.8%与96.6%的准确率,在自建数据集的两种工况中分别取得了97.43%与98.14%的准确率。结果表明：文中提出的模型能够高精度识别故障类型,且相较于其他算法模型,在不同工况下均表现出良好的准确性与泛化性,可为船舶动力齿轮箱的故障诊断提供技术参考。

关键词: 故障诊断, 正则化阈值小波降噪, 马尔可夫转移场, 密集连接神经网络, 压缩-激励网络模块