Key Laboratory of Advanced Equipment Intelligent Manufacturing Technology of Yunnan Province, Kunming University of Science & Technology, Kunming 650500, China.
Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, China.
Sensors (Basel). 2023 Jan 19;23(3):1144. doi: 10.3390/s23031144.
Aiming at the problem that a single neural network model has difficulty in accurately predicting trends of the remaining useful life of a rolling bearing, a method of predicting the remaining useful life of rolling bearings using a gated recurrent unit-deep autoregressive model (GRU-DeepAR) with an adaptive failure threshold was proposed. First, time domain and frequency domain features were extracted from the rolling bearing vibration signal. Second, its operation process was divided into a smooth operation stage and degradation stage according to the trend of the accumulated root mean square of maximum. Then, the failure threshold for different bearings were determined adaptively by the maximum of the smooth operation data. The degradation dataset of a rolling bearing was subsequently obtained. In the meantime, a GRU-DeepAR model was built to obtain predictions of the failure time and failure probability. Appropriate model parameters were determined after a large number of tests to assure the effectiveness and prediction accuracy. Finally, the trend of time series and failure times were predicted by inputting the degradation dataset into the GRU-DeepAR model. Experiments showed that the proposed method can effectively improve the accuracy of the remaining useful life prediction of a rolling bearing with good stability.
针对单一神经网络模型难以准确预测滚动轴承剩余使用寿命趋势的问题，提出了一种使用具有自适应故障阈值的门控循环单元-深度自回归模型（GRU-DeepAR）预测滚动轴承剩余使用寿命的方法。首先，从滚动轴承振动信号中提取时域和频域特征。其次，根据最大累积均方根的趋势，将其运行过程分为平稳运行阶段和退化阶段。然后，通过平稳运行数据的最大值自适应确定不同轴承的故障阈值，从而得到退化数据集。同时，建立了一个 GRU-DeepAR 模型，以获得故障时间和故障概率的预测。经过大量试验确定了合适的模型参数，以保证有效性和预测精度。最后，通过将退化数据集输入到 GRU-DeepAR 模型中，预测时间序列和故障时间的趋势。实验表明，该方法可以有效地提高滚动轴承剩余使用寿命预测的准确性，具有良好的稳定性。
