基于迁移学习和同步蒸馏剪枝算法的燃气轮机剩余使用寿命预测模型优化

IF 9.4 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL
Yu Zheng, Liang Chen, Xiangyu Bao, Fei Zhao, Jingshu Zhong, Chenhan Wang
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引用次数: 0

摘要

对于深度学习(DL)模型在复杂设备剩余使用寿命(RUL)预测和预测性维护(PdM)领域的应用,训练数据不足和模型庞大是两个主要问题。针对这些问题,提出了一种基于迁移学习的模型训练方法和同步蒸馏剪枝算法。通过引入先验知识,设计了三种迁移学习模式,以减少对训练数据的需求。此外,还设计了同步蒸馏剪枝算法使模型轻量化,并采用迭代剪枝方法修剪大型神经网络模型。通过分析不同迁移学习模式的性能,可以证明所提方法的有效性。比较了剪枝前后的模型参数数量和性能。结果表明,在不明显改变预测性能的情况下,所提出的模型具有明显减少模型参数数量的能力。基于所提出的方法,可以有效解决 DL 模型所遇到的数据不足和效率问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction model optimization of gas turbine remaining useful life based on transfer learning and simultaneous distillation pruning algorithm
For the application of deep learning (DL) models in the field of remaining useful life (RUL) prediction and predictive maintenance (PdM) of complex equipment, the insufficient training data and large model are the two major problems. To address these issues, a model training method based on transfer learning and a simultaneous distillation pruning algorithm were proposed. By introducing prior knowledge, three transfer learning modes are devised to reduce the demand of training data. Additionally, the simultaneous distillation pruning algorithm was devised to make the model lightweight, and an iterative pruning method was adopted to trim the large neural network model. By analyzing the performance of different transfer learning modes, the effectiveness of the proposed method can be demonstrated. The number of model parameters and the performance before and after pruning were compared. The results demonstrated that, without significant alterations to the prediction performance, the proposed model exhibited the capability to markedly reduce the number of model parameters. Based on the proposed methods, the challenges of insufficient data and efficiency encountered by DL models could be effectively addressed.
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来源期刊
Reliability Engineering & System Safety
Reliability Engineering & System Safety 管理科学-工程:工业
CiteScore
15.20
自引率
39.50%
发文量
621
审稿时长
67 days
期刊介绍: Elsevier publishes Reliability Engineering & System Safety in association with the European Safety and Reliability Association and the Safety Engineering and Risk Analysis Division. The international journal is devoted to developing and applying methods to enhance the safety and reliability of complex technological systems, like nuclear power plants, chemical plants, hazardous waste facilities, space systems, offshore and maritime systems, transportation systems, constructed infrastructure, and manufacturing plants. The journal normally publishes only articles that involve the analysis of substantive problems related to the reliability of complex systems or present techniques and/or theoretical results that have a discernable relationship to the solution of such problems. An important aim is to balance academic material and practical applications.
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