Meng Fannian, Wang Liujie, Li Hao, Du Wenliao, Gong Xiaoyun, Wu Changjun, Luo Shuangqiang
Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China.
Anyang Cigarette Factory, China Tobacco Industry Co., Ltd., Anyang, 455004, China.
Sci Rep. 2024 Dec 28;14(1):31185. doi: 10.1038/s41598-024-82452-1.
Aiming at the difficulty of extracting vibration data under actual working conditions of rolling bearings, this paper proposes a bearing reliability evaluation method based on generative adversarial network sample enhancement and maximum entropy method under the condition of few samples. Based on generative adversarial network, data sample enhancement under few samples is carried out, and the reliability analysis model is established by using the maximum entropy principle and Poisson process. The reliability is evaluated according to the reliability variation frequency, variation speed and variation acceleration. The analysis results show that with the gradual increase of running time, the reliability variation frequency shows a nonlinear growth trend, which can be roughly divided into the initial running-in stage, the stable running-in stage and the intense running-in stage. The reliability variation speed is then used to distinguish the specific starting time of the three stages, and finally the preliminary relationship between the reliability variation acceleration and the remaining life is obtained. The experimental results of the XJTU-SY dataset show that compared with the existing reliability evaluation model, the proposed model has the advantages of less samples, no need for preprocessing and higher accuracy. The proposed model has made a beneficial supplement to the existing reliability analysis methods.
针对滚动轴承实际工况下振动数据提取困难的问题，提出一种基于生成对抗网络样本增强和最大熵方法的少样本条件下轴承可靠性评估方法。基于生成对抗网络，对少样本数据样本进行增强，并利用最大熵原理和泊松过程建立可靠性分析模型。根据可靠性变化频率、变化速度和变化加速度对可靠性进行评估。分析结果表明，随着运行时间的逐渐增加，可靠性变化频率呈非线性增长趋势，大致可分为初始磨合阶段、稳定磨合阶段和剧烈磨合阶段。然后利用可靠性变化速度区分三个阶段的具体起始时间，最终得到可靠性变化加速度与剩余寿命之间的初步关系。XJTU-SY数据集的实验结果表明，与现有可靠性评估模型相比，所提模型具有样本少、无需预处理、精度高等优点。所提模型对现有可靠性分析方法进行了有益补充。
