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Predicting Remaining Useful Life of Rolling Bearings Based on Reliable Degradation Indicator and Temporal Convolution Network with the Quantile Regression

机译：基于可靠的降解指示器与Quantile回归的可靠性降解指示器和时间卷积网络预测剩余的滚动轴承使用寿命

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Qiaoping Tian; Honglei Wang;
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1. Remaining Useful Life Prediction of Rolling Bearings Based on Multiscale Convolutional Neural Network with Integrated Dilated Convolution Blocks [J] . Ran Wang, Ruyu Shi, Xiong Hu, Shock and vibration . 2021,第a期

机译：基于多尺度卷积神经网络的滚动轴承剩余的使用寿命预测，具有集成扩张卷积块
2. A convolutional neural network based degradation indicator construction and health prognosis using bidirectional long short-term memory network for rolling bearings [J] . Yiwei Cheng, Kui Hu, Jun Wu, Advanced engineering informatics . 2021,第Apra期

机译：基于卷积神经网络的降解指示器建设和健康预测，用于滚动轴承的双向短期内存网络
3. Remaining Useful Life Prediction and Fault Diagnosis of Rolling Bearings Based on Short-Time Fourier Transform and Convolutional Neural Network [J] . Shuang Zhou, Maohua Xiao, Petr Bartos, Shock and vibration . 2020,第1期

机译：基于短时傅里叶变换和卷积神经网络的滚动轴承剩余的使用寿命预测和故障诊断
4. Direct Remaining Useful Life Prediction for Rolling Bearing Using Temporal Convolutional Networks [C] . Chongdang Liu, Linxuan Zhang, Cheng Wu IEEE Symposium Series on Computational Intelligence . 2019

机译：基于时间卷积网络的滚动轴承直接剩余使用寿命预测
5. A new stress-based fatigue life model for rolling bearings [D] . Yu, Wei-Kuei 1999

机译：基于应力的滚动轴承新疲劳寿命模型
6. Time Series Multiple Channel Convolutional Neural Network with Attention-Based Long Short-Term Memory for Predicting Bearing Remaining Useful Life [O] . Jehn-Ruey Jiang, Juei-En Lee, Yi-Ming Zeng 2020

机译：时间序列多通道卷积神经网络具有基于注意力的长短期记忆可预测轴承剩余使用寿命
7. Discrete Cosine Transformation and Temporal Adjacent Convolutional Neural Network-Based Remaining Useful Life Estimation of Bearings [O] . Yu Pang, Limin Jia, Zhan Liu 2020

机译：离散余弦变换和时间相邻的卷积神经网络，其余的轴承使用寿命估算

Predicting Remaining Useful Life of Rolling Bearings Based on Reliable Degradation Indicator and Temporal Convolution Network with the Quantile Regression

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