基于DBN-LSTM的滚动轴承剩余寿命预测模型

Remaining Useful Life Prediction Model for Rolling Bearings Based on DBN-LSTM

针对滚动轴承退化数据的复杂性和传统的寿命预测方法不能充分利用数据的相关性从而导致预测精度不高的问题,提出了一种基于融合深度置信神经网络(deep belief neural,DBN)和长短时记忆神经网络(long-short term memory,LSTM)的剩余寿命预测模型。该模型首先采用带通滤波降噪对滚动轴承振动数据进行去噪,然后依据均方根特征和峭度特征在轴承全寿命周期内的趋势图确定模型的预测起始点;其次利用优化后的4层DBN网络完成深度特征提取并用于LSTM的训练与测试。通过轴承全寿命周期试验证明提出模型的可靠性,并且与传统LSTM、BP(back propagation)神经网络和DBN-BP模型的预测结果进行对比,验证了本文模型的有效性。

In view of the complexity of degraded data of rolling bearings and the problem that traditional life prediction methods can not make full use of the correlation of data,thus the prediction accuracy is not high,a residual life prediction model combining deep belief neural(DBN)network and long-short term memory(LSTM)neural network was proposed.In this model,band-pass filter was first used to denoise the vibration data of rolling bearings,and then the prediction starting point of the model was determined according to the trend diagrams of root mean square and kurtosis features in the whole life cycle of the bearing.Secondly,the optimized 4-layer DBN network was used to extract depth features and be used in LSTM training and testing.The reliability of the proposed model is proved through the bearing life cycle test,and compared with the prediction result of traditional LSTM、BP neural network and DBN-BP,the validity of the model in this paper is verified.