基于灰色模型与LSTM网络的旋转机械轴承寿命预测

大型机械设备中旋转机械占到总量的80%, 为及时掌握其工作状态, 开展如何旋转机械轴承的寿命预测精度的仿真研究。首先, 通过可靠性数值(confidential value, CV)量化评估工作状态; 然后, 利用数据变换和累加积分的方法优化数据平滑性与背景值来改进灰色模型; 并与长短时记忆网络结合为新预测模型来预测系统工作状态; 最后, 将平均绝对百分比误差等3种性能指标与单一模型对比, 将预测失效时刻与全卷积层神经网络算法和无迹粒子滤波算法对比。结果表明, 组合模型预测退化趋势3种指标的平均值优于3种单一模型; 组合模型预测的失效时刻相比于另外两种改进算法更准确。

Life prediction of bearings in rotating machinery based on grey model and LSTM network

Rotating machinery account for 80 percent of the total large mechanical equipment. In order to grasp the working condition in time, the simulation research about how to improve the accuracy of life prediction for the bearings in rotating machineries is carried out. Firstly, the confidential value (CV) is used to quantify the evaluation of the working status. Then, the method of data transformation and cumulative integration is used to optimize the smoothness of the data and the background value to improve the grey model. And the long-short term memory network (LSTM) is combined with a new prediction model to predict the working state of bearings. Finally, the average absolute percentage error and other two performance indicators are compared with the single models, and the predicted failure time is compared with fully convolutional layer neural network algorithm and unscented particle filter algorithm. The results show that the average value of the three indicators for predicting the degradation trend of the combined model is better than that of the three single models. The failure time predicted by the combined model is more accurate than the two improved algorithms.