基于形态波动一致性偏移距离的滚动轴承剩余寿命预测方法

Residual life prediction method of rolling bearing based on morphology fluctuation conformance deviation distance

针对滚动轴承完全失效阈值的设置多根据人工经验选取、退化轨迹适配忽略时间序列整体形态趋势变化的问题,提出一种基于形态波动一致性偏移距离的滚动轴承失效阈值设置与剩余寿命预测方法。首先,引入前向差分(FD)对振动信号进行预处理,并对处理后的信号计算均方根(RMS)值作为退化指标(DI);其次,融合双指数模型对DI曲线进行拟合确定最终参考轴承的完全失效阈值(TFT),降低TFT的设置偏差;最后,利用形态波动一致性偏移距离(MFCDD)计算DI曲线相似度,完成对测试轴承失效阈值的设置,并利用粒子滤波更新双指数模型完成滚动轴承的剩余使用寿命(RUL)预测。在XJTY-SY数据集上的实验结果表明,滚动轴承RUL预测的score得分较动态时间规整匹配方法、卷积神经网络-双向长短期记忆网络预测方法分别提升了82.97%和73.64%;在PHM2012数据集上的实验结果表明,滚动轴承RUL预测的score得分较动态时间规整匹配方法、卷积神经网络-双向长短期记忆网络预测方法、长短期记忆-自注意力机制预测方法分别提升了99.99%、60.65%和99.90%。

Aiming at the problem that the setting of the complete failure threshold of rolling bearings is mostly selected according to artificial experience,and the degradation trajectory adaptation ignores the overall morphological trend change of the time series,a method for setting the failure threshold and predicting the remaining life of rolling bearings based on the consistent offset distance of morphological fluctuation is proposed.Firstly,the forward difference(FD)is introduced to preprocess the vibration signal,and the root mean square(RMS)value of the processed signal is calculated as the degradation indicator(DI).Secondly,the double exponential model is used to fit the DI curve to determine the total failure threshold(TFT)of the final reference bearing,so as to reduce the setting deviation of TFT.Finally,the similarity of the DI curve is calculated by using the morphology fluctuation conformance deviation distance(MFCDD)to complete the setting of the failure threshold of the test bearing,and the remaining useful life(RUL)prediction of the rolling bearing is completed by using the particle filter to update the double exponential model.The experimental results on the XJTY-SY dataset show that the score of rolling bearing RUL prediction is 82.97%and 73.64%higher than that of dynamic time warping matching method,convolutional neural network and bidirectional long short-term memory network prediction method,respectively.The experimental results on the PHM2012 dataset show that the score of rolling bearing RUL prediction is 99.99%,60.65%and 99.90%higher than that of dynamic time warping matching method,convolutional neural network and bidirectional long-term and short-term memory network prediction method,long-term and short-term memory and self-attention mechanism prediction method.