基于PCA和预测误差法的轴承故障预测方法研究

Study on Bearing Fault Prediction Method Based on PCA and Prediction Error Method

在轴承故障诊断中,及时判断出故障具有重要的意义,针对该问题,提出一种基于主成分分析(PCA)和预测误差法的轴承故障预测方法,该方法利用主成分分析方法提取出反映轴承故障的健康监测指标,将该指标作为状态空间的输入,利用预测误差法求解状态空间模型,并对轴承健康监测指标进行预测。结果表明,预测结果与历史数据一致,通过预测值与设定的阈值的比较,可以实现提前预警的目的。

In bearing fault diagnosis,it is of great significance to judge the fault in time. In order to solve this problem,a bearing fault prediction method based on principal component analysis(PCA) and prediction error method is proposed. This method uses principal component method to extract the health monitoring indicator reflecting bearing fault. This index is used as the input of state space. The prediction error method is used to solve the state space model,and the bearing health monitoring indicator is predicted. The results show that the prediction results are consistent with the historical data,and the purpose of early warning can be achieved by comparing the predicted values with the set threshold.