针对实际工况下故障样本不足导致诊断精度低以及大型网络模型在有限算力下效率低和存储高,提出了基于格拉姆角和场(GASF)图像编码与迁移压缩神经网络(SqueezeNet)结合的故障诊断方法. 首先,采用GASF编码将一维轴承信号转变为具有时间关联性的二维图像,使数据特征即使在小样本条件下也包含丰富完整的故障信息. 然后,将数据集ImageNet中SqueezeNet全局池化层之前经过预训练的参数迁移至新SqueezeNet,并且将Sgdm作为优化算法,减少训练时间和存储.最后,通过迁移后SqueezeNet对GASF图像进行特征提取和分类,进而构成GSN故障诊断模型. 为了验证小样本下基于GASF和SqueezeNet的轴承故障诊断方法,选用不同规模的数据集进行测试,与先进算法对比分析,并与其他网络模型在相同条件下进行迁移学习对比研究.结果表明,该方法具有较高的故障识别准确率99.15%,且具有更强的小样本识别能力和更高效的轻量化能力.
A fault diagnosis method based on the combination of Gramian angle sum fields (GASF) image encoding and transfer compression neural network (SqueezeNet) is proposed to address the low diagnostic accuracy caused by insufficient fault samples under actual working conditions, as well as the low efficiency and high storage issues of large network models under limited computing power. Firstly, the GASF encoding method is used to transform the one-dimensional bearing signal into a two-dimensional image with temporal correlation, so that the data features contain rich and complete fault information even under small sample conditions. Next, the parameters before the global pooling layer in the pre-trained SqueezeNet on the ImageNet dataset are transferred to the new SqueezeNet, and Sgdm is used as an optimization algorithm to reduce training time and storage. Then, the transferred SqueezeNet is used to extract and classify features from the GASF images, ultimately forming a GSN fault diagnosis model. In order to verify the feasibility and superiority of the proposed method, datasets of different scales were selected for testing, and transfer learning comparisons were conducted with other advanced network models under the same conditions, as well as a comparative analysis with advanced algorithms. The experimental results show that the proposed method has a high fault recognition accuracy of 99.15%, while exhibiting and has stronger small-sample recognition ability and improved more efficient lightweight ability.
[1] Rai A,Upadhyay S H.A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings[J].Tribology International,2016,96:289-306.
[2] Lu C,Wang Z Y,Qin W L,et al.Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification[J].Signal Processing,2017,130:377-388.
[3] Shi Q,Zhang H.Fault diagnosis of an autonomous vehicle with an improved SVM algorithm subject to unbalanced datasets[J].IEEE Transactions on Industrial Electronics,2021,68(7):6248-6256.
[4] Li S B,Liu G K,Tang X H,et al.An ensemble deep convolutional neural network model with improved D-S evidence fusion for bearing fault diagnosis[J].Sensors,2017,17(8):1729 -1747.
[5] 雷亚国,贾峰,周昕,等.基于深度学习理论的机械装备大数据健康监测方法[J].机械工程学报,2015,51(21):49-56.
[6] 乔志城,刘永强,廖英英.改进经验小波变换与最小熵解卷积在铁路轴承故障诊断中的应用[J].振动与冲击,2021,40(2):81-90.
[7] 何勇,王红,谷穗.一种基于遗传算法的VMD 参数优化轴承故障诊断新方法[J].振动与冲击,2021,40(6 ):184-189.
[8] 李路云,王海瑞.基于GAF-Alexnet-ELM的齿轮箱故障诊断方法[J].农业装备与车辆工程,2023,61(5):81-86.
[9] 杨魏华,阮爱国,黄国勇.基于预训练GoogleNet模型和迁移学习的齿轮箱故障检测方法[J].机电工程,2024,41(2):262-270.
[10] 段晓燕,焦孟萱,雷春丽,等.基于MTF-MSMCNN的小样本滚动轴承故障诊断方法[J].航空动力学报,2024,39(1):240-252.
[11] 姚齐水,别帅帅,余江鸿,等.一种结合改进Inception V2模块和CBAM的轴承故障诊断方法[J].振动工程学报,2022,35(4):949-957.
[12] 虞浒,缪小冬,顾寅骥,等.基于GAF-DarkNet的滚动轴承故障诊断方法[J].轴承,2024(2):66-73.
[13] 吴宽.基于图像编码和深度学习的滚动轴承智能故障诊断方法[D].赣州:江西理工大学,2022:58-73.
[14] 侯东晓,周子安,程荣财,等.基于GADF-TL-ResNeXt的滚动轴承故障诊断方法[J].计量学报,2023,44(10):1534-1542.
[15] 孙昊,郑建明.基于MTF-DFT的小型残差网络轴承故障诊断[J].机电工程技术,2024,53(4):316-320.
[16] Xiong J,He Z G,Zhou Q J,et al.Photovoltaic glass edge defect detection based on improved SqueezeNet[J].Signal,Image and Video Processing,2024,18(3):2841-2856.
