低通筛选优化神经架构搜索的风电齿轮箱边缘侧故障诊断方法

doi:10.3901/JME.2025.07.361

机械工程学报 ›› 2025, Vol. 61 ›› Issue (7): 361-372.doi: 10.3901/JME.2025.07.361

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低通筛选优化神经架构搜索的风电齿轮箱边缘侧故障诊断方法

吴艳灵, 汤宝平, 邓蕾, 付豪

重庆大学高端装备机械传动全国重点实验室重庆 400044

收稿日期:2024-04-13 修回日期:2024-12-22 发布日期:2025-05-12
作者简介:吴艳灵，女，1990年出生，博士研究生。主要研究方向为深度学习模型的轻量化技术与旋转机械的智能诊断。E-mail：yanlingwu@cqu.edu.cn
汤宝平(通信作者)，男，1971年出生，博士，教授，博士研究生导师。主要研究方向为机电装备安全服役与退化趋势、测试计量技术及仪器、无线传感器网络等。E-mail：bptang@cqu.edu.cn
基金资助:
国家自然科学基金(52375082，52275087)、中央高校科研业务费专项资金(2023CDJXY-025)和重庆市研究生科研创新(CYS23139)资助项目。

Edge-side Fault Diagnosis of Wind Turbine Gearboxes by Low-pass Screening Neural Architecture Search

WU Yanling, TANG Baoping, DENG Lei, FU Hao

State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing 400044

Received:2024-04-13 Revised:2024-12-22 Published:2025-05-12

摘要/Abstract

摘要： 边缘侧故障诊断要求深度学习模型轻量化，但现有的模型设计方法多为手动设计，费时耗力，同时设计的模型不考虑边缘硬件可配置的资源容量，使得设计的模型可能无法满足部署要求。基于此，提出了一种低通筛选优化的神经架构搜索算法，在考虑硬件可配置资源容量时，为边缘硬件自动设计故障诊断模型。首先，设计一个经验启发的搜索空间以降低轻量化和高精度模型的搜索难度，然后建立一个低通筛选奖励函数，引导智能体在搜索过程中迭代的筛选低于硬件可配置资源容量条件的轻量化诊断模型，最后采用帕累托支配得到竞争性帕累托最优解集，并为边缘硬件选择最优的部署模型实现风电齿轮箱的边缘侧故障诊断。最终通过行星齿轮箱试验和实际应用案例分析对所提方法的有效性和可行性进行验证。结果表明搜索的模型在准确率、浮点数计算量、参数量方面均优于对比模型。特别是在应用案例中，相比最优性能的深度模型GoogLeNet-v1和边缘友好模型MobileNet-v2，搜索的模型LSNAS-Netb准确率提升3.06%和3.65%，同时参数量和浮点数计算量仅达到GoogLeNet-v1的1/15.56、1/5.47，MobileNet-v2的1/6.19、1/1.18。

关键词: 边缘故障诊断, 神经架构搜索, 低通筛选, 模型轻量化, 风电齿轮箱

Abstract: Edge-side fault diagnosis requires lightweight deep models. They are, typically, empirically handcrafted by experts, which is time-consuming and labor-intensive. The configurable resource capacity for the edge hardware is not considered in manual lightweight models; therefore, they may not meet deployment requirements. Here, a method based on a low-pass screening neural architecture search is proposed. Fault-diagnosis models are automatically designed for edge hardware considering the hardware configurable resource capacity. First, an empirically inspired search space is designed to reduce the search difficulty in lightweight models. Meanwhile, a low-pass screening reward function is modeled to guide an agent iteratively screening lightweight diagnostic models meeting the hardware configurable resource capacity condition during the search process. Finally, Pareto-optimal domination is used to obtain a competitive Pareto-optimal solution set, providing an optimal model for the edge hardware to achieve fault diagnosis of wind-turbine gearboxes. The feasibility and effectiveness of the method were verified on a gearbox’s test from a drivetrain diagnostics simulator and on measured wind-farm case. The results indicate that the searched models are superior to the competing models in terms of accuracy, FLOPs, and parameters. Particularly, in the application case, LSNAS-Netb achieved 3.06% and 3.65% higher accuracies compared with the deep model GoogLeNet-v1 and edge-side-friendly MobileNet-v2, respectively, with 15.56×and 6.19×fewer parameters and 5.47×and 1.18×fewer FLOPs, respectively.

Key words: edge-side fault diagnosis, neural architecture search, low-pass screening, lightweight models, wind-turbine gearboxes

中图分类号:

TH17

吴艳灵, 汤宝平, 邓蕾, 付豪. 低通筛选优化神经架构搜索的风电齿轮箱边缘侧故障诊断方法[J]. 机械工程学报, 2025, 61(7): 361-372.

WU Yanling, TANG Baoping, DENG Lei, FU Hao. Edge-side Fault Diagnosis of Wind Turbine Gearboxes by Low-pass Screening Neural Architecture Search[J]. Journal of Mechanical Engineering, 2025, 61(7): 361-372.

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低通筛选优化神经架构搜索的风电齿轮箱边缘侧故障诊断方法

Edge-side Fault Diagnosis of Wind Turbine Gearboxes by Low-pass Screening Neural Architecture Search

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