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Generative knowledge-based transfer learning for few-shot health condition estimation
Complex & Intelligent Systems ( IF 5.8 ) Pub Date : 2022-08-20 , DOI: 10.1007/s40747-022-00787-6
Weijie Kang, Jiyang Xiao, Junjie Xue

In the field of high-end manufacturing, it is valuable to study few-shot health condition estimation. Although transfer learning and other methods have effectively improved the ability of few-shot learning, they still cannot solve the lack of prior knowledge. In this paper, by combining data enhancement, knowledge reasoning, and transfer learning, a generative knowledge-based transfer learning model is proposed to achieve few-shot health condition estimation. First, with the effectiveness of data enhancement on machine learning, a novel batch monotonic generative adversarial network (BM-GAN) is designed for few-shot health condition data generation, which can solve the problem of insufficient data and generate simulated training data. Second, a generative knowledge-based transfer learning model is proposed with the performance advantages of the belief rule base (BRB) method on few-shot learning, which combines expert knowledge and simulated training data to obtain a generalized BRB model and then fine-tunes the generalized model with real data to obtain a dedicated BRB model. Third, through uniform sampling of NASA lithium battery data and simulating few-shot conditions, the generative transfer-belief rule base (GT-BRB) method proposed in this paper is verified to be feasible for few-shot health condition estimation and improves the estimation accuracy of the BRB method by approximately 17.3%.



中文翻译:

用于小样本健康状况估计的基于生成知识的迁移学习

在高端制造领域,研究few-shot健康状况估计具有重要意义。尽管迁移学习等方法有效地提高了few-shot learning的能力,但仍然无法解决先验知识的不足。在本文中,通过结合数据增强、知识推理和迁移学习,提出了一种基于生成知识的迁移学习模型,以实现few-shot健康状况估计。首先,利用数据增强对机器学习的有效性,设计了一种新颖的批量单调生成对抗网络(BM-GAN),用于生成少样本的健康状况数据,可以解决数据不足的问题并生成模拟训练数据。第二,结合专家知识和模拟训练数据,结合专家知识和模拟训练数据,得到一个广义的BRB模型,然后对广义的BRB模型进行微调,提出了一种基于生成知识的迁移学习模型。用真实数据建立模型,得到专用的 BRB 模型。第三,通过对NASA锂电池数据的统一采样和few-shot条件的模拟,验证了本文提出的生成转移信念规则库(GT-BRB)方法对于few-shot健康状况估计的可行性,并改进了估计。 BRB 方法的准确度提高了大约 17.3%。它结合专家知识和模拟训练数据得到一个广义的BRB模型,然后用真实数据对广义模型进行微调,得到一个专用的BRB模型。第三,通过对NASA锂电池数据的统一采样和few-shot条件的模拟,验证了本文提出的生成转移信念规则库(GT-BRB)方法对于few-shot健康状况估计的可行性,并改进了估计。 BRB 方法的准确度提高了大约 17.3%。它结合专家知识和模拟训练数据得到一个广义的BRB模型,然后用真实数据对广义模型进行微调,得到一个专用的BRB模型。第三,通过对NASA锂电池数据的统一采样和few-shot条件的模拟,验证了本文提出的生成转移信念规则库(GT-BRB)方法对于few-shot健康状况估计的可行性,并改进了估计。 BRB 方法的准确度提高了大约 17.3%。

更新日期:2022-08-20
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