直升机传动系统主减速器健康监测技术研究进展

刘文正; 朱如鹏; 朱欣华; 李苗苗; 周文广; 佘亦曦

doi:10.13224/j.cnki.jasp.20250064

直升机传动系统主减速器健康监测技术研究进展

doi: 10.13224/j.cnki.jasp.20250064

1.
南京航空航天大学直升机传动技术国家级重点实验室，南京 210016
2.
中国航发湖南动力机械研究所直升机传动技术国家级重点实验室，湖南株洲 412002

基金项目: 某传动预研专项（KY-1044-2023-0493）

详细信息

作者简介:
刘文正（1997-），男，工程师，博士，从事直升机传动系统动力学及健康监测研究。E-mail：wz_liu@nuaa.edu.cn

通讯作者:
朱如鹏（1959-），男，教授，博士，从事航空动力传动技术、先进机械结构和系统设计理论研究。E-mail：rpzhu_nuaa@163.com

中图分类号: V275.1；TP306.3
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- 文章访问数: 124
- HTML浏览量: 57
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- 被引次数: 0
出版历程
- 收稿日期: 2025-02-10
- 网络出版日期: 2026-07-30

Research progress on health monitoring technology for helicopter main gearbox in transmission systems

1.
National Key Laboratory of Science and Technology on Helicopter Transmission，Nanjing University of Aeronautics and Astronautics，Nanjing 210016，China
2.
National Key Laboratory of Science and Technology on Helicopter Transmission，Hunan Aviation Powerplant Research Institute，Aero Engine Corporation of China，Zhuzhou Hunan 412002，China

摘要

摘要:
随着低空经济的快速发展和直升机应用场景的不断拓展，直升机的安全性、维护性和可靠性要求持续升级。主减速器作为直升机传动系统的核心部件，其健康状态直接影响整机服役寿命与飞行安全。然而，主减复杂的结构与苛刻的工况使得健康监测面临信号成分复杂、特征提取困难、传感器布置受限等挑战。近三十年来，围绕直升机主减速器健康监测技术开展了大量研究，主要涵盖振动信号处理、状态监测指标、传感器技术、机器学习与数字孪生等方向。通过对比分析各类技术的优势边界与适用局限，总结未来研究应聚焦于构建系统化全域研究体系、发展多源传感信息融合技术、增强状态指标物理可解释性，深化数字孪生及迁移学习的工程化应用。研究成果可为主减速器健康监测系统的优化提供技术支撑，提升直升机安全性、可靠性及维护效率。
- 直升机 /
- 主减速器 /
- 健康监测 /
- 信号处理 /
- 智能诊断
Abstract:
With the rapid growth of the low-altitude economy and expanding helicopter applications, demands for safety, maintainability, and reliability are increasing. As the core of the transmission system, the main gearbox’s health directly impacts service life and flight safety. However, its complex structure and harsh operating conditions pose challenges such as signal complexity, feature extraction difficulties, and sensor placement limitations. Over the past three decades, research on helicopter main gearbox health monitoring has focused on vibration signal processing, condition monitoring indicators, sensor technology, machine learning, and digital twins. Studies have analyzed the advantages and limitations of these technologies. Future efforts should emphasize establishing a systematic research framework, advancing multi-source sensor fusion, enhancing the interpretability of condition monitoring indicators, and promoting the engineering application of digital twins and transfer learning. These findings support optimizing main gearbox health monitoring systems, enhancing helicopter safety, reliability, and maintenance efficiency.
- helicopter /
- main gearbox /
- health monitoring /
- signal processing /
- intelligent diagnosis

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图 1 某型直升机主减速器结构^[8]

Figure 1. Structure of the main gearbox of a certain type of helicopter^[8]

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图 2 通过振动分析进行状态维护^[11]

Figure 2. Condition-based maintenance through vibration analysis^[11]

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图 3 TSA处理流程^[14]

Figure 3. Flowchart of TSA Processing^[14]

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图 4 UH-60A变速箱底板裂纹引起的TSA能量变化^[16]

Figure 4. TSA energy variation caused by cracks in the bottom plate of the UH-60A gearbox^[16]

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图 5 CS-29试验台及其小波分解信号对比^[25]

Figure 5. Comparison of CS-29 test bench and its wavelet decomposition signal^[25]

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图 6 行星轮系振动信号分离过程^[29]

Figure 6. Planetary gear train vibration signal separation process^[29]

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图 7 OH-58直升机试验台及传感器布置^[29]

Figure 7. OH-58 500HP helicopter transmission test bench and sensor layout^[29]

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图 8 轴承故障信号分离结果^[31]

Figure 8. Bearing fault signal separation results^[31]

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图 9 Bell 412 直升机HUMS传感器位置及故障样本^[33]

Figure 9. Bell 412 helicopter HUMS sensor locations and fault sample conditions^[33]

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图 10 FM4计算流程^[38]

Figure 10. FM4 calculation process^[38]

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图 11 弧齿锥齿轮故障试验台及指标对比^[41]

Figure 11. Spiral bevel gear fault test bench and index comparison^[41]

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图 12 光纤传感器及加速度传感器布置及结果对比^[48]

Figure 12. Arrangement of fiber optic sensors and acceleration sensors and comparison of results^[48]

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图 13 直升机传动试验台滑油磨粒监测结果^[52]

Figure 13. 500HP helicopter transmission test bench lubricating ODM results^[52]

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图 14 声发射无线传输方案^[62]

Figure 14. AE Wireless Transmission Solution^[62]

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图 15 用于传递HUMS数据的机器学习算法流程^[64]

Figure 15. ML algorithm flow for delivering HUMS data^[64]

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图 16 基于传感器融合的自适应加权卷积神经网络^[69]

Figure 16. Adaptive channel weighted convolutional neural network based on sensor fusion^[69]

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图 17 基于数字孪生的传动系统故障诊断^[75]

Figure 17. Transmission system fault diagnosis based on digital twin^[75]

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