火箭推进

涡轮泵轴承是可重复使用火箭的关键,因此能够有效提取出轴承故障特征频率从而展开故障诊断十分重要。将奇异值分解(SVD)和包络谱解调法相结合,对火箭涡轮泵轴承展开故障特征提取。对轴承内环、外环及滚动体故障数据进行处理和分析,结果表明,针对信号中含有大量噪声的数据,相比传统的包络谱解调法,改进方法的故障特征提取效果明显提升。通过该方法提取出的3种故障低频特征频率的相对幅值相比于传统包络谱解调均有提升。同时,可以有效降低高频噪声的干扰,尤其在高频区依然可以看到比较明显的特征频率,而传统包络谱解调法的高频区基本被噪声覆盖。通过计算得出信号的信噪比均有60 dB以上的提升。

Turbo pump bearings are the key to reusable rockets. Therefore, it is very important to extract the bearing fault characteristic frequency effectively to carry out fault diagnosis. Singular value decomposition(SVD)and envelope spectrum demodulation are combined to extract the fault features of rocket turbopump bearings. By processing and analyzing the fault data of the bearing's inner ring, outer ring and rolling element, the results show that compared with the traditional envelope spectrum demodulation method, the improved method has significantly improved the fault feature extraction effect for the data with a lot of noise in the signal. Compared with traditional envelope spectrum demodulation, the relative amplitudes of three kinds of fault low frequency characteristic frequencies extracted by this method are improved. At the same time, it can effectively reduce the interference of high frequency noise. Especially in the high frequency region, characteristic frequency can still be seen more obviously, while the high frequency region of the traditional envelope spectrum demodulation method is basically covered by noise. Through calculation, the signal-to-noise ratio of the signal is improved by more than 60 dB.

引言
1 故障特征提取方法
2 故障仿真数据故障特征提取
3 模拟实验数据故障特征提取
4 结论

图1 简单正弦信号奇异值分解处理<br/>Fig.1 Singular value decomposition of simple sinusoidal signal

图1 简单正弦信号奇异值分解处理
Fig.1 Singular value decomposition of simple sinusoidal signal

表1 标准正弦信号特征参数表<br/>Tab.1 Standard sinusoidal signal characteristic parameters

表1 标准正弦信号特征参数表
Tab.1 Standard sinusoidal signal characteristic parameters

图2 包络谱解调原理<br/>Fig.2 Envelope spectrum demodulation principle

图2 包络谱解调原理
Fig.2 Envelope spectrum demodulation principle

图3 信号y时域图<br/>Fig.3 Time domain diagram of signal y

图3 信号y时域图
Fig.3 Time domain diagram of signal y

图4 信号y频域图<br/>Fig.4 Frequency domain diagram of signal y

图4 信号y频域图
Fig.4 Frequency domain diagram of signal y

图5 信号y频谱和包络频谱对比<br/>Fig.5 Comparison between the frequency spectrum and envelope spectrum of signal y

图5 信号y频谱和包络频谱对比
Fig.5 Comparison between the frequency spectrum and envelope spectrum of signal y

图6 改进包络谱解调法实现步骤<br/>Fig.6 Implementation steps of the improved envelope spectrum demodulation method

图6 改进包络谱解调法实现步骤
Fig.6 Implementation steps of the improved envelope spectrum demodulation method

图7 改进包络谱解调与传统包络谱解调对比<br/>Fig.7 Comparison between improved envelope spectrum demodulation and traditional envelope spectrum demodulation

图7 改进包络谱解调与传统包络谱解调对比
Fig.7 Comparison between improved envelope spectrum demodulation and traditional envelope spectrum demodulation

表2 N1006轴承结构参数<br/>Tab.2 Structural parameters of the N1006 bearing

表2 N1006轴承结构参数
Tab.2 Structural parameters of the N1006 bearing

表3 故障类型及相应故障特征频率<br/>Tab.3 Fault types and corresponding fault charac-teristic frequencies

表3 故障类型及相应故障特征频率
Tab.3 Fault types and corresponding fault charac-teristic frequencies

表4 处理信号的信噪比<br/>Tab.4 Signal-to-noise ratio of the processed signal

表4 处理信号的信噪比
Tab.4 Signal-to-noise ratio of the processed signal

图8 仿真数据处理结果<br/>Fig.8 Processed simulated results

图8 仿真数据处理结果
Fig.8 Processed simulated results

图9 实验器整体图<br/>Fig.9 Overall diagram of the experimental apparatus

图9 实验器整体图
Fig.9 Overall diagram of the experimental apparatus

图10 故障轴承
Fig.10 Faulty bearing

表5 轴承故障信息<br/>Tab.5 Fault information of bearings

表5 轴承故障信息
Tab.5 Fault information of bearings

表6 处理信号的信噪比<br/>Tab.6 The signal-to-noise ratio of the processed signal

表6 处理信号的信噪比
Tab.6 The signal-to-noise ratio of the processed signal

图11 实验数据处理结果<br/>Fig.11 Processed experimental results

图11 实验数据处理结果
Fig.11 Processed experimental results

[1] 马帅, 郭健鑫, 周磊, 等. 固体火箭发动机技术发展综述[J]. 火箭推进, 2023, 49(2): 1-14.
[2]郑雄, 杨勇, 姚世东, 等. 法尔肯9可重复使用火箭发展综述[J]. 导弹与航天运载技术, 2016(2): 39-46.
[3]姚尚鹏, 黄红, 赵佳敏, 等. 涡轮泵典型故障仿真与辨识系统设计[J]. 火箭推进, 2023, 49(3): 96-104.
[4]李军星,朱文进,邱明,等.基于EM-IKF协同算法的滚动轴承剩余寿命预测[J/OL].航空动力学报,[2023-09-27].https://doi.org/10.13224/j.cnki.jasp.20230251.
[5]王国彪, 何正嘉, 陈雪峰, 等. 机械故障诊断基础研究“何去何从”[J]. 机械工程学报, 2013, 49(1): 63-72.
[6]赵志宏. 基于振动信号的机械故障特征提取与诊断研究[D]. 北京: 北京交通大学, 2012.
[7]李珊珊. 基于冲击脉冲法的便携式轴承故障检测仪的设计与改进[J]. 仪表技术与传感器, 2014(4): 106-107.
[8]RANDALL R B. A history of cepstrum analysis and its application to mechanical problems[J]. Mechanical Systems and Signal Processing, 2017, 97: 3-19.
[9]TONG S G, FU Z L, TONG Z M, et al. Fault diagnosis for gearboxes based on Fourier decomposition method and resonance demodulation[J]. Journal of Zhejiang University-Science A(Applied Physics & Engineering), 2023, 24(5): 404-419.
[10]GAO H Z, LIANG L, CHEN X G, et al. Feature extraction and recognition for rolling element bearing fault utilizing short-time Fourier transform and non-negative matrix factorization[J]. Chinese Journal of Mechanical Engineering, 2015, 28(1): 96-105.
[11]何正嘉, 袁静, 訾艳阳. 机械故障诊断的内积变换原理与应用[M]. 北京: 科学出版社, 2012.
[12]张振海, 王维庆, 王海云, 等. 基于改进小波包的风电机组齿轮箱复合故障特征提取研究[J]. 太阳能学报, 2022, 43(9): 331-336.
[13]YAN R Q, GAO R X, CHEN X F. Wavelets for fault diagnosis of rotary machines: a review with applications[J]. Signal Processing, 2014, 96: 1-15.
[14]范文健, 毛万鑫, 吴疆. 车辆加速度信号的EMD和IIR滤波联合降噪方法[J]. 振动与冲击, 2021, 40(20):307-312.
[15]LEI Y G, LIN J, HE Z J, et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery[J]. Mechanical Systems and Signal Processing, 2013, 35(1/2): 108-126.
[16]GOLAFSHAN R, YUCE SANLITURK K. SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults[J]. Mechanical Systems and Signal Processing, 2016, 70/71: 36-50.
[17]陈恩利, 张玺, 申永军, 等. 基于SVD降噪和盲信号分离的滚动轴承故障诊断[J]. 振动与冲击, 2012, 31(23): 185-190.
[18]尹进田, 唐杰, 刘丽, 等. 参数同步优化随机共振在牵引传动系统早期微弱故障诊断中的应用[J]. 振动与冲击, 2021, 40(17): 234-240.
[19]谯自健, 束学道. 非对称势诱导随机共振增强机械重复瞬态提取[J]. 机械工程学报, 2021, 57(23): 160-168.
[20]朱娟娟, 高丙朋, 王维庆. 基于随机共振的轴承微弱故障信号检测[J]. 轴承, 2021(12): 43-48.

备注

引言

1 故障特征提取方法

2 故障仿真数据故障特征提取

3 模拟实验数据故障特征提取

4 结论

期刊信息