Prognostics With Variational Autoencoder by Generative Adversarial Learning
Prognostics With Variational Autoencoder by Generative Adversarial Learning
复制标题
通过生成对抗学习使用变分自动编码器进行预测
DOI：
10.1109/tie.2021.3053882
复制
发表时间：
2022
影响因子：
7.7
通讯作者：
VanZwieten, James
中科院分区：
文献类型：
--
作者：
Huang, Yu;Tang, Yufei;VanZwieten, James
预测学根据历史和当代数据预测在役系统的未来性能、进展和剩余使用寿命(RUL)。预测学的挑战之一是开发能够处理现实世界不确定性的方法，这些不确定性通常会导致不准确的预测。为了减少不确定性的影响，实现准确的退化轨迹和RUL预测，提出了一种新的基于产生式对抗网络训练的变分自动编码器的序列到序列预测模型。利用长短期记忆网络和高斯混合模型作为构建块，使该模型能够提供概率预测。为了减少原始数据带来的不确定性，采用相关性和单调性度量来识别退化过程中的敏感特征。然后，将选择的特征与单热点健康状态指标串联作为模型的训练数据，以学习寿命结束，而不需要故障阈值的先验知识。通过从真实航空发动机、风力涡轮机和锂离子电池收集的健康监测数据验证了所提出模型的性能。结果表明，在长期退化进度和RUL预测任务中，可以获得显著的性能提升。
Prognostics predicts the future performance progression and remaining useful life (RUL) of in-service systems based on historical and contemporary data. One of the challenges in prognostics is the development of methods that are capable of handling real-world uncertainties that typically lead to inaccurate predictions. To alleviate the impacts of uncertainties and to achieve accurate degradation trajectory and RUL predictions, a novel sequence-to-sequence predictive model is proposed based on a variational autoencoder that is trained with generative adversarial networks. A long short-term memory network and a Gaussian mixture model are utilized as building blocks so that the model is capable of providing probabilistic predictions. Correlative and monotonic metrics are applied to identify sensitive features in the degradation progress, in order to reduce the uncertainty induced from raw data. Then, the selected features are concatenated with one-hot health state indicators as training data for the model to learn end of life without the need for prior knowledge of failure thresholds. Performance of the proposed model is validated by health monitoring data collected from real-world aeroengines, wind turbines, and lithium-ion batteries. The results demonstrate that significant performance improvement can be achieved in long-term degradation progress and RUL prediction tasks.
使用 Auto ML 提高黑色素瘤诊断系统的性能
DOI：
--
发表时间：
2023
期刊：
影响因子：
--
作者：
Inoue Manabu;Yoshimoto Takeshi;Tanaka Kanta;Koge Junpei;Shiozawa Masayuki;Nishii Tatsuya;Ohta Yasutoshi;Fukuda Tetsuya;Satow Tetsu;Kataoka Hiroharu;Yamagami Hiroshi;Ihara Masafumi;Koga Masatoshi;Mlynash Michael;Albers Gregory W.;Toyoda Kazunori;正木達也・北畠直人・飛塚丈輝・花崎和寿・張 維倫・永岡 隆
通讯作者：
正木達也・北畠直人・飛塚丈輝・花崎和寿・張 維倫・永岡 隆
使用物理信息神经网络阐明电子传输特性
DOI：
--
发表时间：
2023
期刊：
影响因子：
--
作者：
Kawaguchi S;Takahashi K;Satoh K;川口悟
通讯作者：
川口悟