Huo Dongyue, Kang Yuyun, Wang Baiyang, Feng Guifang, Zhang Jiawei, Zhang Hongrui
School of Information Science and Engineering, Linyi University, Linyi 276000, China.
School of Logistics, Linyi University, Linyi 276000, China.
Entropy (Basel). 2022 Nov 6;24(11):1618. doi: 10.3390/e24111618.
The gearbox is an important component in the mechanical transmission system and plays a key role in aerospace, wind power and other fields. Gear failure is one of the main causes of gearbox failure, and therefore it is very important to accurately diagnose the type of gear failure under different operating conditions. Aiming at the problem that it is difficult to effectively identify the fault types of gears using traditional methods under complex and changeable working conditions, a fault diagnosis method based on multi-sensor information fusion and Visual Geometry Group (VGG) is proposed. First, the power spectral density is calculated with the raw frequency domain signal collected by multiple sensors before being transformed into a power spectral density energy map after information fusion. Second, the obtained energy map is combined with VGG to obtain the fault diagnosis model of the gear. Finally, two datasets are used to verify the effectiveness and generalization ability of the method. The experimental results show that the accuracy of the method can reach 100% at most on both datasets.
变速箱是机械传动系统中的一个重要部件，在航空航天、风力发电等领域发挥着关键作用。齿轮故障是变速箱故障的主要原因之一，因此在不同工况下准确诊断齿轮故障类型非常重要。针对传统方法在复杂多变工况下难以有效识别齿轮故障类型的问题，提出了一种基于多传感器信息融合和视觉几何组（VGG）的故障诊断方法。首先，利用多个传感器采集的原始频域信号计算功率谱密度，经信息融合后转化为功率谱密度能量图。其次，将得到的能量图与VGG相结合，得到齿轮的故障诊断模型。最后，使用两个数据集验证该方法的有效性和泛化能力。实验结果表明，该方法在两个数据集上的准确率最高可达100%。
