PDF(Pubmed)
Abstract：
In the context of Industry 4.0, bearings, as critical components of machinery, play a vital role in ensuring operational reliability. The detection of their health status is thus of paramount importance. Existing predictive models often focus on point predictions of bearing lifespan, lacking the ability to quantify uncertainty and having room for improvement in accuracy. To accurately predict the long-term remaining useful life (RUL) of bearings, a novel time convolutional network model with an attention mechanism-based soft thresholding decision residual structure for quantifying the lifespan interval of bearings, namely TCN-AM-GPR, is proposed. Firstly, a spatio-temporal graph is constructed from the bearing sensor signals as the input to the prediction model. Secondly, a residual structure based on a soft threshold decision with a self-attention mechanism is established to further suppress noise in the collected bearing lifespan signals. Thirdly, the extracted features pass through an interval quantization layer to obtain the RUL and its confidence interval of the bearings. The proposed methodology has been verified using the PHM2012 bearing dataset, and the comparison of simulation experiment results shows that TCN-AM-GPR achieved the best point prediction evaluation index, with a 2.17% improvement in R2 compared to the second-best performance from TCN-GPR. At the same time, it also has the best interval prediction comprehensive evaluation index, with a relative decrease of 16.73% in MWP compared to the second-best performance from TCN-GPR. The research results indicate that TCN-AM-GPR can ensure the accuracy of point estimates, while having superior advantages and practical significance in describing prediction uncertainty.
摘要：
在工业4.0的背景下，轴承、作为机械的关键部件，在确保运行可靠性方面发挥着至关重要的作用。因此，检测他们的健康状况至关重要。现有的预测模型通常侧重于轴承寿命的点预测，缺乏量化不确定性的能力，在准确性方面还有改进的空间。为了准确预测轴承的长期剩余使用寿命（RUL），一种新颖的时间卷积网络模型，具有基于注意力机制的软阈值决策残差结构，用于量化轴承的寿命间隔，即TCN-AM-GPR，是提议的。首先,从轴承传感器信号构建时空图作为预测模型的输入。其次,建立了基于软阈值决策的残差结构，并具有自注意机制，以进一步抑制采集到的轴承寿命信号中的噪声。第三，提取的特征通过区间量化层得到轴承的RUL及其置信区间。所提出的方法已经使用PHM2012轴承数据集进行了验证，仿真实验结果对比表明,TCN-AM-GPR取得了最佳点预测评价指标,与TCN-GPR的第二好性能相比，R2提高了2.17%。同时,具有最佳区间预测综合评价指标,与TCN-GPR的第二好性能相比，MWP相对降低了16.73%。研究结果表明,TCN-AM-GPR能够保证点估计的准确性,在描述预测不确定性方面具有优越的优势和现实意义。
