Abstract: Marine air compressors work in a harsh environment, with multiple internal and external excitation sources. The collected noise signals are strongly time-varying, resulting in low fault diagnosis accuracy and thus difficulties in effective identification of various faults of marine air compressors. To address this problem, a fault diagnosis method combining Gramian Angular field (GAF) and Convolutional Neural Network (CNN) is proposed. Firstly, the basic principles, methods and implementation procedures of GAF and CNN are explained. Then, various faults of marine air compressors are simulated through experiments, and the noise signals of each fault are collected. The GAF is used to transform the one-dimensional time-domain signals into two-dimensional images, mapping the characteristic information to texture features such as colors and points of the two-dimensional images. Finally, the two-dimensional images are fed into CNN for feature extraction and fault diagnosis. The experimental results show that the proposed method can effectively identify various faults of the marine air compressor with a diagnostic accuracy of 99.2% superior to other algorithms, while ensuring the operational efficiency. It can provide technical references for the application of intelligent fault diagnosis systems for ships.

Key words: marine air compressor, noise analysis, Gramian angular field(GAF), convolutional neural network(CNN), fault diagnosis

摘要： 船用空压机工作环境恶劣,内外激励源众多,采集的噪声信号具有强烈的时变性,会导致故障诊断精度较低,难以实现船用空压机各类故障的有效识别。为此,该文提出将格拉姆角场（Gramian angular field,GAF）编码和卷积神经网络（convolutional neural network,CNN） 法相结合的故障诊断方法。首先,阐述了GAF和CNN的基本原理、方法和实施步骤;然后,通过试验模拟了船用空压机的各类故障,并采集相应噪声信号,再利用GAF将一维时域信号转换为二维图像,将特征信息映射为二维图像的颜色、点等纹理特征;最后,将二维图像输入至CNN中进行特征提取和故障诊断。试验结果表明：在保证运行效率的前提下,该方法能够有效识别船用空压机的各类故障,诊断精度达到99.2%,优于其他算法,可为船舶故障智能诊断的应用提供了新途径和新思路。

关键词: 船用空压机, 噪声分析, 格拉姆角场, 卷积神经网络, 故障诊断