College of Petroleum and Chemical Engineering, Jingzhou University, Jingzhou 434000, Hubei, China.
School of Chemistry and Chemical Engineering, Huanggang Normal University, Jingzhou 438000, Hubei, China.
Comput Intell Neurosci. 2022 Aug 18;2022:9841443. doi: 10.1155/2022/9841443. eCollection 2022.
In order to solve the problem that variable working conditions and fault types cannot be diagnosed in gear fault diagnosis of petroleum drilling equipment, four kinds of faults, namely, gear broken tooth, gear crack, gear pitting, and gear wear, are studied in this paper. Based on the SOM neural network algorithm, an intelligent diagnosis model of gear fault is proposed, and the PCA method is used to reduce data dimension and fuse features. The state index of life prediction is determined, and the remaining service life prediction of gear transmission system is predicted based on exponential degradation model. The results show that the accuracy of the SOM model for fault diagnosis is high, and the bearing in gearbox can be replaced or repaired in advance according to the residual life curve, so as to achieve the purpose of predictive maintenance.
为了解决石油钻井设备齿轮故障诊断中工况和故障类型多变的问题，本文研究了齿轮断齿、齿轮裂纹、齿轮点蚀和齿轮磨损等四种故障。基于 SOM 神经网络算法，提出了一种齿轮故障智能诊断模型，并采用 PCA 方法降低数据维度并融合特征。确定寿命预测的状态指标，基于指数退化模型预测齿轮传动系统的剩余使用寿命。结果表明，SOM 模型对故障诊断的准确率较高，可根据剩余寿命曲线提前更换或修复变速箱中的轴承，从而达到预测性维护的目的。
