改进注意力机制的航空发动机实验转子系统智能故障诊断

PDF(1627 KB)

振动与冲击 ›› 2024, Vol. 43 ›› Issue (4) : 261-269.

论文

伍济钢，文港，杨康

作者信息 +

Improved attention mechanism for intelligent fault diagnosis of experimental rotor systems in aero engines

WU Jigang, WEN Gang, YANG Kang

Author information +

文章历史 +

摘要

考虑到航空发动机的工作环境十分恶劣，其故障的振动信号特征隐蔽且噪声干扰严重，为了加强网络对振动信号中关键特征的提取能力，提出了改进注意力机制的航空发动机转子系统智能故障诊断方法对航空发动机转子系统的不平衡和碰摩等故障进行诊断。提出局部池化改进的通道注意力机制，能够通过预提取局部极值解决现有通道注意力机制对航空发动机转子故障通道信息提取能力不足的问题；提出多评分机制改进的空间注意力机制，能够通过不同尺度的卷积评分解决现有空间注意力机制对航空发动机转子故障空间信息提取能力不足的问题；将二者结合构建改进的通道空间注意力机制模块，再导入一维卷积神经网络中构建改进注意力机制的一维卷积神经网络完成智能故障诊断，并且通过航空发动机转子系统故障数据集对比分析实验证明了该网络优秀的检测性能、抗噪性能和泛化性能等综合性能以及注意力机制改进方法的可行性。

Abstract

Considering the harsh working environment of aero-engine, the hidden characteristics of the vibration signal of the fault and the serious noise interference, in order to strengthen the extraction ability of the network to the key features in the vibration signal, an intelligent fault diagnosis method for aero-engine rotor system with improved attention mechanism is proposed to diagnose the faults of aero-engine rotor system such as unbalance and friction. The proposed improved channel attention mechanism with partial pooling can solve the problem of insufficient information extraction capability of the existing channel attention mechanism for aero-engine rotor fault channels by pre-extracting local extreme values; the improved spatial attention mechanism with multiple scoring mechanism is proposed, which can solve the problem that the existing channel attention mechanism has insufficient ability to extract spatial information about aero-engine rotor faults by convolution scoring of different scales, The above methods are combined to build an improved attention mechanism module in channel space, and then imported into a 1D convolutional neural network to build a 1D convolutional neural network with improved attention mechanism to complete intelligent fault diagnosis, and the comprehensive performance of the network such as excellent detection performance, noise immunity and generalization performance as well as the feasibility of the attention mechanism improvement method are demonstrated by the comparative analysis of the aero-engine rotor system fault dataset.

导出引用

伍济钢，文港，杨康. 改进注意力机制的航空发动机实验转子系统智能故障诊断[J]. 振动与冲击, 2024, 43(4): 261-269

WU Jigang, WEN Gang, YANG Kang. Improved attention mechanism for intelligent fault diagnosis of experimental rotor systems in aero engines[J]. Journal of Vibration and Shock, 2024, 43(4): 261-269

参考文献

[1]张晗, 王兴, 田毅, 等. 稀疏低秩协同正则优化及航空轴承故障诊断[J]. 西安交通大学学报, 2021, 55(11): 46-58. ZHANG Han, WANG Xin, TIAN Yi, et al. Collaborative Sparse Low Rank Regularization for Aero-Engine Bearing Fault Diagnosis[J]. Journal of xi’an Jiaotong University, 2021, 55(11): 46-58. [2]HANG W T, JI X F, HUANG J, et al. Compound fault diagnosis of aero-engine rolling element bearing based on CCA blind extraction[J]. IEEE Access, 2021, 9: 159873-159881. [3]韩磊, 洪杰, 王冬. 基于小波包分析的航空发动机轴承故障诊断[J]. 推进技术, 2009, 30(3): 328-331+341. HAN Lei, HONG Jie, WANG Dong. Fault diagnosis of aero-engine bearings based on wavelet package analysis[J]. Journal of propulsion technology, 2009, 30(3): 328-331+341. [4]LI H H, GOU L F, ZHENG H, et al. Intelligent fault diagnosis of aeroengine sensors using improved pattern gradient spectrum entropy[J]. International Journal of Aerospace Engineering, 2021, 2021(1): 1-20. [5]王奉涛, 刘晓飞, 敦泊森, 等. 基于萤火虫优化的核自动编码器在中介轴承故障诊断中的应用[J]. 机械工程学报, 2019, 55(7): 58-64. WANG Fengtao, LIU Xiaofei, DUN Bosen, et al. Application of Kernel Auto-encoder Based on Firefly Optimization in Intershaft Bearing Fault Diagnosis[J]. Journal of Mechanical Engineering, 2019, 55(7): 58-64. [6]王月, 赵明航, 刘雪云, 等. 基于孪生减元注意力网络的航空发动机故障诊断[J/OL]. 航空动力学报: 1-9[2022-11-11]. WANG Yue, ZHAO Minghang, LIU Xueyun, et al. Siamese Reduced-neuron Attention Networks for Aero-engine Fault Diagnosis[J/OL], Journal of Aerospace Power: 1-9[2022-11-11]. [7]黄鑫, 张小栋, 张英杰, 等. 基于改进DBNs的三维叶尖间隙叶片裂纹诊断方法[J]. 振动.测试与诊断, 2022, 42(2): 213-219+402. HUANG Xin, ZHANG Xiaodong, ZHANG Yingjie, et al. Fault Diagnosis for Three-Dimension Blade Tip Clearance Based on Turbine Blade Crack by New Improved Deep Belief Networks[J]. Journal of Vibration, Measurement & Diagnosis, 2022, 42(2): 213-219+402. [8]王奉涛, 薛宇航, 王洪涛, 等.GLT-CNN方法及其在航空发动机中介轴承故障诊断中的应用[J]. 振动工程学报, 2019, 32(6): 1077-1083. WANG Fengtao, XUE Yuhang, WANG Hongtao, et al. GLT-CNN and its application of aero-engine intermediary bearing fault diagnosis[J]. Journal of vibration engineering, 2019, 32(6): 1077-1083. [9]康玉祥, 陈果, 尉询楷, 等.基于残差网络的航空发动机滚动轴承故障多任务诊断方法[J]. 振动与冲击, 2022, 41(16): 285-293. KANG Yuxiang, CHEN Guo, WEI Xunkai, et al. A multi-task fault diagnosis method of rolling bearings based on the residual network[J]. Journal of vibration and shock, 2022, 41(16): 285-293. [10]李宏宇, 苏越, 陈康, 等.基于频域特征的航空轴承智能诊断[J/OL]. 航空动力学报: 1-11[2022-11-11]. LI Hongyu, SU Yue, CHEN Kang, et al. Intelligent diagnosis of aviation bearings based on frequency domain features[J/OL]. Journal of Aerospace Power:1-11[2022-11-11]. [11]韩淞宇, 邵海东, 姜洪开, 等. 基于提升卷积神经网络的航空发动机高速轴承智能故障诊断[J]. 航空学报, 2022, 43(9): 158-171. HAN Songyu, SHAO Haidong, JIANG Hongkai, et al. Intelligent fault diagnosis of aero-engine high-speed bearings using enhanced CNN[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(9): 158-171. [12] HE Q, PANG Y, JIANG G, et al. A spatio-temporal multiscale neural network approach for wind turbine fault diagnosis with imbalanced SCADA data[J]. IEEE Transactions on Industrial Informatics, 2020, PP(99): 1-1. [13]田科位, 董绍江, 姜保军, 等.基于改进深度残差网络的轴承故障诊断方法[J]. 振动与冲击, 2021, 40(20): 247-254. TIAN Kewei, DONG Shaojiang, JIANG Baojun, et al. A bearing fault diagnosis method based on an improved depth residual network [J]. Journal of vibration and shock, 2021, 40(20): 247-254. [14]牛锐祥, 丁华, 施瑞, 等.改进密集连接卷积网络的滚动轴承故障诊断方法[J]. 振动与冲击, 2022,41(11): 252-258. NIU Ruiyang DING Hua, SHI Rui, et al. Rolling bearing fault diagnosis method based on improved densely connected convolution network[J]. Journal of vibration and shock, 2022, 41(11): 252-258. [15]MNIH V, HEESS N, GRAVES A. Recurrent models of visual attention[J]. Advances in neural information processing systems, 2014, 27. [16]BAHDANAU D, CHO K, BENGIO Y. Neural machine translation by jointly learning to align and translate［EB/OL］. [17]HU J, SHEN L, SUN G, et al. Squeeze-and-excitation networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018. [18]CHEN L, ZHANG H W, XIAO J, et al. SCA- CNN: spatial and channel-wise attention in convolutional networks for image captioning[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, USA, 2017. [19]ALMAHAIRI A, BALLAS N, COOIJMANS T, et al. Dynamic capacity networks [C]//International Conference on Machine Learning, New York, USA, 2016: 2549-2558. [20]ROY A G, NAVAB N, WACHINGER C. Concurrent spatial and channel ‘squeeze & excitation in fully convolutional networks[C]//International conference on medical image computing and computer-assisted intervention. Springer, Cham, 2018: 421-429. [21]WOO S, PARK J, LEE J Y, et al. CBAM: Convolutional block attention module[C]. Proceedings of the European Conference on Computer Vision (ECCV), 2018: 3-19. [22]杨建国, 孙扬, 郑严. 基于小波和模糊神经网络的涡喷发动机故障诊断[J]. 推进技术, 2001(2): 114-117. YANG Jianguo, SUN Yang, ZHENG Yan. Fault diagnosis for turbo engine based on wavelet and fuzzy neural network [J]. Journal of Propulsion Technology, 2001(2): 114-117. [23]许子非, 金江涛, 李春. 基于多尺度卷积神经网络的滚动轴承故障诊断方法[J]. 振动与冲击, 2021,40(18): 212-220. XU Zifei, JIN Jiangtao, LI Chun. New method for the fault diagnosis of rolling bearings based on a multiscale convolutional neural network[J]. Journal of Vibration and Shock, 2021, 40(18): 212-220. [24]程建刚, 毕凤荣, 张立鹏, 等. 基于MACNN的柴油机故障诊断方法研究[J]. 振动与冲击, 2022,41(10): 8-15. CHENG Jiangang, BI Fengrong, ZHANG Lipeng, et al. Fault diagnosis method for diesel engines based on MACNN[J]. Journal of vibration and shock, 2022, 41(10): 8-15. [25]刘洋, 程强, 史曜炜, 等. 基于注意力模块及1D-CNN的滚动轴承故障诊断[J]. 太阳能学报, 2022, 43(3): 462-468. LIU Yang, CHENG Qiang, SHI Yaowei, et al. FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON ATTENTIONMODULE AND 1D-CNN[J]. Acta energiae solaris sinica. [26]杨柳, 王钰. 泛化误差的各种交叉验证估计方法综述[J].计算机应用研究, 2015, 32(5): 1287-1290+1297. YANG Liu, WANG Yu. Survey for various cross-validation estimators of generalization error[J]. Application Research of Computers, 2015, 32(5): 1287-1290+1297. [27]SMITH W A, RANDALL R B. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study[J]. Mechanical Systems and Signal Processing, 2015, 64: 100-131. [28]KUMAR A, ZHOU Y Q, GANDHI C.P., et al. (2020) Bearing defect assessment using wavelet transform based deep convolutional neural network (DCNN). Alexandria Engineering Journal 59, 999–1012 (Elsevier).