) 上海交通大学 机械与动力工程学院, 上海 200240
收稿日期:2020-06-09出版日期:2021-11-28发布日期:2021-12-03通讯作者:殷跃红E-mail:yhyin@sjtu.edu.cn作者简介:姜宇迪(1995-),男,江苏省苏州市人,硕士生,主要从事机器人和智能制造方面的研究.基金资助:特种设备安全防护系统及其部件产品功能安全性能测试及评价关键技术研究(2018YFC0808903)Unsupervised Transfer Learning for Remaining Useful Life Prediction of Elevator Brake
JIANG Yudi, HU Hui, YIN Yuehong() School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Received:2020-06-09Online:2021-11-28Published:2021-12-03Contact:YIN Yuehong E-mail:yhyin@sjtu.edu.cn摘要/Abstract
摘要： 为了改善电梯制动器在真实工作环境下的寿命预测效果,提出一种基于长短期记忆网络自编码器(LSTM-ED)的无监督深度迁移学习方法,利用仿真数据实现制动器在工作时的健康状态分析.利用源领域数据初步训练LSTM-ED和全连接网络;以LSTM-ED为特征提取器,将仿真和实际数据映射到特征空间并利用最大平均差异实现数据对齐;利用全连接网络回归特征空间中的目标领域数据,从而实现对制动器在真实工作环境下的剩余生命周期预测.在训练阶段中,采用分步训练法替代传统的联合训练法,以保证单个模块的准确性.对比试验仿真数据与电梯塔中的实际工作数据,以验证方法的有效性.结果表明:通过引入迁移学习和分步训练法,所提方法可以将剩余生命周期预测的均方误差降低至 0.0016,能够实现电梯制动器在真实工作环境下的剩余生命周期精准预测.
关键词: 电梯制动器, 无监督深度迁移学习, 长短期记忆网络自编码器, 剩余生命周期, 分步训练
Abstract: In order to improve the life prediction effect of elevator brake in the real working environment, an unsupervised deep transfer learning (UDTL) method based on long short-term memory encoder-decoder (LSTM-ED) was proposed. The simulation data were used to analyze the health status of brake when it was working. First, the LSTM-ED and the fully connected network were initially trained through the source domain data. Then, the LSTM-ED was used as a feature extractor to map the simulated and actual data to the feature space, and the maximum mean discrepancy was adopted to achieve data alignment. Finally, the target domain data in the feature space was regressed through the fully connected network to predict the remaining useful life (RUL) of the real brake. In the training phase, a step-by-step training method was used to ensure the accuracy of a single module. The validity was verified by comparing the experimental simulation data with the real working data in the elevator tower. The results show that by introducing the transfer learning and step-by-step training methods, the proposed method can reduce the mean square error of RUL prediction to 0.0016, and can achieve accurate RUL prediction of elevator brakes in real working environment.
Key words: elevator brake, unsupervised deep transfer learning (UDTL), long short-term memory encoder-decoder (LSTM-ED), remaining useful life (RUL), step training
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