基于DRS与改进Autogram的风电齿轮箱复合故障特征提取

doi:10.11930/j.issn.1004-9649.202303124

摘要/Abstract

摘要：

复合故障特征提取是分析风电齿轮箱故障根因的关键。提出基于离散随机分离（DRS）和改进Autogram的复合故障特征提取方法。基于DRS方法削弱振动信号周期性成分对微弱故障成分的影响，结合谱峭度与谱负熵设计一种新的特征量化指标，对最大重叠离散小波包变换与无偏自相关处理后的各窄带分量进行综合评价，以选择最优的滤波频带，精确地识别包含复合故障特征的信号分量。将所提方法应用于实际风电齿轮箱齿轮-轴承复合故障诊断中，能够有效提取出振动信号中的多个故障特征，具有较好的诊断效果。

关键词: 风电机组, 复合故障, 离散随机分离, 故障诊断, 特征提取

Abstract:

Compound fault feature extraction is the key to analyzing the root cause of wind power gearbox faults. A compound fault feature extraction method based on DRS and improved Autogram is proposed. Based on the DRS method, the influence of the periodic components of vibration signals on the weak fault components is reduced. A new feature quantification index of spectral kurtosis and spectral negative entropy is designed to comprehensively evaluate the narrow-band components after maximum overlapping discrete wavelet packet transform and unbiased autocorrelation processing, so as to select the optimal filtering frequency band and accurately identify the signal components containing compound fault features. The method in this paper is applied to the compound fault diagnosis of wind power gearbox and bearing, which can effectively extract multiple fault features from vibration signals and has a good diagnostic effect.

Key words: wind turbine, compound fault, discrete random separation, fault diagnosis, feature extraction

马海飞, 滕伟, 彭迪康, 柳亦兵, 靳涛. 基于DRS与改进Autogram的风电齿轮箱复合故障特征提取[J]. 中国电力, 2023, 56(10): 71-79.

Haifei MA, Wei TENG, Dikang PENG, Yibing LIU, Tao JIN. Compound Fault Feature Extraction of Wind Power Gearbox Based on DRS and Improved Autogram[J]. Electric Power, 2023, 56(10): 71-79.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202303124

https://www.electricpower.com.cn/CN/Y2023/V56/I10/71

图/表 11

图 1 短时振动序列构造原理

Fig.1 Principle of short time vibration sequence construction

图 2 风电齿轮箱复合故障特征提取流程

Fig.2 Flow chart of compound fault feature extraction for wind power gearbox

表 1 风力发电机齿轮箱基本信息

Table 1 Wind power gearbox information

部位	齿数	个数
行星级内齿圈	92	1
行星级行星轮	36	3
行星级太阳轮	20	1
一级平行轴低速齿轮	94	1
一级平行轴中速小齿轮	21	1
二级平行轴中速大齿轮	125	1
二级平行轴高速齿轮	92	1

表 2 轴的转频、啮合频率

Table 2 Rotating frequency and meshing frequency of shaft

转频	频率	阶次信息
输入轴	0.3	0.01005
低速轴	1.87	0.0627
中速轴	7.54	0.2528
高速轴	29.83	1

啮合频率	频率	阶次信息
行星级	34.4	1.1532
中速级	165.8	5.5582
高速级	716	24

表 3 高速轴后轴承的故障特征频率及其对应阶次

Table 3 Fault feature frequency and corresponding order of high-speed shaft rear bearing

部位	故障特征频率/Hz	故障特征阶次
保持架	12.0	0.402
滚动体	74.3	2.490
外圈	168.5	5.650
内圈	249.0	8.340

图 3 角度域信号的阶次分析

Fig.3 Order analysis of angle domain signal

图 4 DRS-改进Autogram的组合平方包络阶次谱

Fig.4 Combined squared envelope order spectrum based on DRS and improved Autogram

图 5 DRS-改进Autogram的增强平方包络阶次谱

Fig.5 Enhanced squared envelope order spectrum based on DRS and improved Autogram

图 6 DRS- Autogram的组合平方包络阶次谱

Fig.6 Combined squared envelope order spectrum based on DRS and Autogram

图 7 DRS- Autogram的增强平方包络阶次谱

Fig.7 Enhanced squared envelope order spectrum based on DRS and Autogram

图 8 齿轮箱拆解结果示意

Fig.8 Gearbox disassembly result

参考文献 24

1	苏向敬, 山衍浩, 周汶鑫, 等. 基于GRU和注意力机制的海上风机齿轮箱状态监测[J]. 电力系统保护与控制, 2021, 49 (24): 141- 149.
	SU Xiangjing, SHAN Yanhao, ZHOU Wenxin, et al. GRU and attention mechanism-based condition monitoring of an offshore wind turbine gearbox[J]. Power System Protection and Control, 2021, 49 (24): 141- 149.
2	李东东, 赵阳, 赵耀, 等. 基于深度特征融合网络的风电机组行星齿轮箱故障诊断方法[J]. 电力系统保护与控制, 2022, 50 (10): 1- 10.
	LI Dongdong, ZHAO Yang, ZHAO Yao, et al. A fault diagnosis method for a wind turbine planetary gearbox based on a deep feature fusion network[J]. Power System Protection and Control, 2022, 50 (10): 1- 10.
3	丁显, 柳亦兵, 滕伟. 风电机组齿轮箱非平稳振动信号谱分析方法[J]. 中国电力, 2017, 50 (12): 153- 158.
	DING Xian, LIU Yibing, TENG Wei. Spectrum analysis of nonstationary vibration signal for wind turbine gear box[J]. Electric Power, 2017, 50 (12): 153- 158.
4	姜锐, 滕伟, 刘潇波, 等. 风电机组发电机轴承电腐蚀故障的分析诊断[J]. 中国电力, 2019, 52 (6): 128- 133.
	JIANG Rui, TENG Wei, LIU Xiaobo, et al. Diagnosis of electrical corrosion fault in wind turbine generator bearing based on vibration signal analysis[J]. Electric Power, 2019, 52 (6): 128- 133.
5	DHAMANDE L S, CHAUDHARI M B. Compound gear-bearing fault feature extraction using statistical features based on time-frequency method[J]. Measurement, 2018, 125, 63- 77. DOI
6	TANG G, LUO G G, ZHANG W H, et al. Underdetermined blind source separation with variational mode decomposition for compound roller bearing fault signals[J]. Sensors, 2016, 16 (6): 897. DOI
7	翟昱尧. 基于改进形态分量分析算法的齿轮箱复合故障诊断研究[D]. 郑州: 华北水利水电大学, 2018.
	ZHAI Yuyao. Research on gearbox compound fault diagnosis based on improved morphological component analysis algorithm[D]. Zhengzhou: North China University of Water Resources and Electric Power, 2018.
8	WAN S T, ZHANG X, DOU L J. Compound fault diagnosis of bearings using improved fast spectral kurtosis with VMD[J]. Journal of Mechanical Science and Technology, 2018, 32 (11): 5189- 5199. DOI
9	ZHAO D Z, LI J Y, CHENG W D, et al. Vold-Kalman generalized demodulation for multi-faults detection of gear and bearing under variable speeds[J]. Procedia Manufacturing, 2018, 26, 1213- 1220. DOI
10	LI N, HUANG W G, GUO W J, et al. Multiple enhanced sparse decomposition for gearbox compound fault diagnosis[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69 (3): 770- 781. DOI
11	代士超, 郭瑜, 伍星. 基于同步平均与倒频谱编辑的齿轮箱滚动轴承故障特征量提取[J]. 振动与冲击, 2015, 34 (21): 205- 209. DOI
	DAI Shichao, GUO Yu, WU Xing. Gear-box rolling bearings' fault features extraction based on cepstrum editing and time domain synchronous average[J]. Journal of Vibration and Shock, 2015, 34 (21): 205- 209. DOI
12	ATANASIU V, DOROFTEI I. Dynamic contact loads of spur gear pairs with addendum modifications[J]. European Journal of Mechanical and Environmental Engineering, 2008, 49 (2): 27- 32.
13	UMEZAWA K, SUZUKI T, SATO T. Vibration of power transmission helical gears: approximate equation of tooth stiffness[J]. Bulletin of JSME, 1986, 29 (251): 1605- 1611. DOI
14	CHEN B, PENG F Y, WANG H Y, et al. Compound fault identification of rolling element bearing based on adaptive resonant frequency band extraction[J]. Mechanism and Machine Theory, 2020, 154, 104051. DOI
15	王志坚, 张纪平, 王俊元, 等. 基于MED-MOMEDA的风电齿轮箱复合故障特征提取研究[J]. 电机与控制学报, 2018, 22 (9): 111- 118. DOI
	WANG Zhijian, ZHANG Jiping, WANG Junyuan, et al. Wind turbine gearbox multi-fault diagnosis based on MED-MOMEDA[J]. Electric Machines and Control, 2018, 22 (9): 111- 118. DOI
16	ABBOUD D, ANTONI J, SIEG-ZIEBA S, et al. Deterministic-random separation in nonstationary regime[J]. Journal of Sound and Vibration, 2016, 362, 305- 326. DOI
17	ANTONI J, RANDALL R B. Unsupervised noise cancellation for vibration signals: part I—evaluation of adaptive algorithms[J]. Mechanical Systems and Signal Processing, 2004, 18 (1): 89- 101. DOI
18	PEETERS C, LECLÈRE Q, ANTONI J, et al. Review and comparison of tacholess instantaneous speed estimation methods on experimental vibration data[J]. Mechanical Systems and Signal Processing, 2019, 129, 407- 436. DOI
19	隆勇, 郭瑜, 伍星, 等. 基于振动信号分离的行星轴承故障特征提取[J]. 振动与冲击, 2020, 39 (13): 78- 83, 109. DOI
	LONG Yong, GUO Yu, WU Xing, et al. Fault feature extraction of planet bearings based on vibration signal separation[J]. Journal of Vibration and Shock, 2020, 39 (13): 78- 83, 109. DOI
20	ANTONI J, RANDALL R B. Unsupervised noise cancellation for vibration signals: part II—a novel frequency-domain algorithm[J]. Mechanical Systems and Signal Processing, 2004, 18 (1): 103- 117. DOI
21	贺东台, 郭瑜, 伍星, 等. 基于离散随机分离的齿轮箱复合故障分析法[J]. 机械强度, 2019, 41 (3): 515- 520. DOI
	HE Dongtai, GUO Yu, WU Xing, et al. Analysis scheme for multi-faults vibration of gearbox based on discrete random separation[J]. Journal of Mechanical Strength, 2019, 41 (3): 515- 520. DOI
22	MOSHREFZADEH A, FASANA A. The Autogram: an effective approach for selecting the optimal demodulation band in rolling element bearings diagnosis[J]. Mechanical Systems and Signal Processing, 2018, 105, 294- 318. DOI
23	ANTONI J. The infogram: Entropic evidence of the signature of repetitive transients[J]. Mechanical Systems and Signal Processing, 2016, 74, 73- 94. DOI
24	刘苗苗. 基于改进自相关图的数控机床滚动轴承早期故障诊断研究[D]. 武汉: 华中科技大学, 2021.
	LIU Miaomiao. Research on incipient fault diagnosis of rolling element bearing based on improved autogram computer numerical control machine tools[D]. Wuhan: Huazhong University of Science and Technology, 2021.

[1]	李丹, 秦世耀, 李少林, 贺敬. 基于混沌粒子群的双馈风电机组LVRT实测建模及暂态参数辨识[J]. 中国电力, 2024, 57(8): 75-84.
[2]	郑鹏, 韩鹏程, 王国栋, 娄颖. 中压配电线路断线高阻接地故障精细化诊断方法[J]. 中国电力, 2024, 57(4): 220-228.
[3]	张博智, 张茹, 焦东翔, 王龙宇, 周一凡, 周丽霞. 基于VMD-SAST的电能质量扰动分类识别方法[J]. 中国电力, 2024, 57(2): 34-40.
[4]	李丹, 梁云嫣, 缪书唯, 方泽仁, 胡越, 贺帅. 基于高斯混合聚类和改进条件变分自编码的多风电场功率日场景生成方法[J]. 中国电力, 2024, 57(12): 17-29.
[5]	刘贺千, 张健, 张海月, 杨洪达, 陈世玉, 申昱博, 王磊. 基于高频阻抗谱的干式空心电抗器匝间短路故障诊断方法[J]. 中国电力, 2024, 57(10): 218-224.
[6]	赵晶晶, 杜明, 刘帅, 李梓博, 马闻鹤. 基于模型预测控制的双馈风电机组调频与转子转速恢复策略[J]. 中国电力, 2023, 56(6): 11-17.
[7]	黎燕, 程馨, 黄祖梁, 杨琢. 高可靠性三相多功能并网变换器研究[J]. 中国电力, 2023, 56(5): 172-181.
[8]	李博浩, 郭昆丽, 吕家君, 蔡维正, 刘璐豪, 刘凤仪, 郝翊帆. 弱电网下改进LADRC抑制直驱风机次同步振荡研究[J]. 中国电力, 2023, 56(4): 56-67.
[9]	李刚, 孟坤, 贺帅, 刘云鹏, 杨宁. 考虑特征耦合的Bi-LSTM变压器故障诊断方法[J]. 中国电力, 2023, 56(3): 100-108,117.
[10]	韩伟, 段文岩, 杜兴伟, 姚峰, 马伟东, 刘磊. 基于数字孪生的在运安控系统故障诊断方法[J]. 中国电力, 2023, 56(11): 121-127.
[11]	李冰, 白云山, 赵宽, 郭聪彬, 翟永杰. 基于HSCA-YOLOv7的风电机组叶片表面缺陷检测算法[J]. 中国电力, 2023, 56(10): 43-52.
[12]	王磊, 柳亦兵, 滕伟, 黄心伟, 刘剑韬. 风电机组叶片无损检测技术研究与进展[J]. 中国电力, 2023, 56(10): 80-95.
[13]	吴宇辉, 张扬帆, 高峰, 王玙, 王耀函, 杨伟新, 张鸿. 基于工作模态分析的风电机组叶片裂纹损伤在线监测研究[J]. 中国电力, 2023, 56(10): 106-114.
[14]	王爽, 罗倩, 唐波, 姜岚, 李锦. 考虑样本类内不平衡的CHPOA-DBN变压器故障诊断方法[J]. 中国电力, 2023, 56(10): 133-144.
[15]	张宏杰, 陈贵凤, 闫宏伟, 杨晓龙, 侯天仁, 张伟. 基于SMOTE与Bayes优化的LSTM网络变压器故障诊断[J]. 中国电力, 2023, 56(10): 164-170.